{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import torch\n",
    "import os\n",
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "import random\n",
    "from zipfile import ZipFile\n",
    "\n",
    "from skimage import io, transform\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from torchvision import transforms, utils\n",
    "from sklearn.utils import shuffle\n",
    "# Ignore warnings\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "from DepthData_mob import DepthDataset\n",
    "from DepthData_mob import Augmentation\n",
    "from DepthData_mob import ToTensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from zipfile import ZipFile\n",
    "zf = ZipFile('/workspace/FILENAME', 'r')\n",
    "zf.extractall('/workspace/')\n",
    "zf.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# traincsv = shuffle(traincsv, random_state=0)\n",
    "traincsv=pd.read_csv('/workspace/data/nyu2_train.csv')\n",
    "traincsv = traincsv.values.tolist()\n",
    "traincsv = shuffle(traincsv, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 (640, 480) (640, 480)\n",
      "1 (640, 480) (640, 480)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#display a sample set of image and depth image\n",
    "depth_dataset = DepthDataset(traincsv=traincsv,root_dir='/workspace/')\n",
    "fig = plt.figure()\n",
    "len(depth_dataset)\n",
    "for i in range(len(depth_dataset)):\n",
    "    sample = depth_dataset[i]\n",
    "    print(i, sample['image'].size, sample['depth'].size)\n",
    "    plt.imshow(sample['image'])\n",
    "    plt.figure()\n",
    "    plt.imshow(sample['depth'])\n",
    "    plt.figure()\n",
    "    if i == 1:\n",
    "        plt.show()\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#testing code for trainloader\n",
    "\n",
    "# depth_dataset = DepthDataset(traincsv=traincsv, root_dir='/workspace/',\n",
    "#                 transform=transforms.Compose([Augmentation(0.5),ToTensor()]))\n",
    "# batch_size=4\n",
    "# train_loader=torch.utils.data.DataLoader(depth_dataset, batch_size, shuffle=True)\n",
    "# dataiter = iter(train_loader)\n",
    "# images, labels = dataiter.next()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#loading the mobilNetDepth model\n",
    "from Mobile_model import Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import kornia \n",
    "\n",
    "def ssim(img1, img2, val_range, window_size=11, window=None, size_average=True, full=False):\n",
    "    ssim = kornia.losses.SSIM(window_size=11,max_val=val_range,reduction='none')\n",
    "    return ssim(img1, img2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.cm\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "def DepthNorm(depth, maxDepth=1000.0): \n",
    "    return maxDepth / depth\n",
    "\n",
    "class AverageMeter(object):\n",
    "    def __init__(self):\n",
    "        self.reset()\n",
    "\n",
    "    def reset(self):\n",
    "        self.val = 0\n",
    "        self.avg = 0\n",
    "        self.sum = 0\n",
    "        self.count = 0\n",
    "\n",
    "    def update(self, val, n=1):\n",
    "        self.val = val\n",
    "        self.sum += val * n\n",
    "        self.count += n\n",
    "        self.avg = self.sum / self.count\n",
    "\n",
    "def colorize(value, vmin=10, vmax=1000, cmap='plasma'):\n",
    "    value = value.cpu().numpy()[0,:,:]\n",
    "\n",
    "    # normalize\n",
    "    vmin = value.min() if vmin is None else vmin\n",
    "    vmax = value.max() if vmax is None else vmax\n",
    "    if vmin!=vmax:\n",
    "        value = (value - vmin) / (vmax - vmin) # vmin..vmax\n",
    "    else:\n",
    "        # Avoid 0-division\n",
    "        value = value*0.\n",
    "    # squeeze last dim if it exists\n",
    "    #value = value.squeeze(axis=0)\n",
    "\n",
    "    cmapper = matplotlib.cm.get_cmap(cmap)\n",
    "    value = cmapper(value,bytes=True) # (nxmx4)\n",
    "\n",
    "    img = value[:,:,:3]\n",
    "\n",
    "    return img.transpose((2,0,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def LogProgress(model, writer, test_loader, epoch):\n",
    "    model.eval()\n",
    "    sequential = test_loader\n",
    "    sample_batched = next(iter(sequential))\n",
    "    image = torch.autograd.Variable(sample_batched['image'].cuda())\n",
    "    depth = torch.autograd.Variable(sample_batched['depth'].cuda(non_blocking=True))\n",
    "    if epoch == 0: writer.add_image('Train.1.Image', vutils.make_grid(image.data, nrow=6, normalize=True), epoch)\n",
    "    if epoch == 0: writer.add_image('Train.2.Depth', colorize(vutils.make_grid(depth.data, nrow=6, normalize=False)), epoch)\n",
    "    output = DepthNorm( model(image) )\n",
    "    writer.add_image('Train.3.Ours', colorize(vutils.make_grid(output.data, nrow=6, normalize=False)), epoch)\n",
    "    writer.add_image('Train.3.Diff', colorize(vutils.make_grid(torch.abs(output-depth).data, nrow=6, normalize=False)), epoch)\n",
    "    del image\n",
    "    del depth\n",
    "    del output\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let's use 6 GPUs!\n",
      "Model created.\n",
      "Epoch: [0][0/792]\tTime 19.918 (19.918)\tETA 4:22:54\tLoss 0.5485 (0.5485)\n",
      "Epoch: [0][5/792]\tTime 1.786 (28.979)\tETA 0:23:25\tLoss 0.7105 (0.7036)\n",
      "Epoch: [0][10/792]\tTime 1.760 (37.884)\tETA 0:22:55\tLoss 0.6309 (0.6915)\n",
      "Epoch: [0][15/792]\tTime 1.818 (46.799)\tETA 0:23:32\tLoss 0.5780 (0.6686)\n",
      "Epoch: [0][20/792]\tTime 1.745 (55.662)\tETA 0:22:27\tLoss 0.5753 (0.6531)\n",
      "Epoch: [0][25/792]\tTime 1.780 (64.463)\tETA 0:22:45\tLoss 0.5840 (0.6406)\n",
      "Epoch: [0][30/792]\tTime 1.800 (73.598)\tETA 0:22:51\tLoss 0.5717 (0.6296)\n",
      "Epoch: [0][35/792]\tTime 1.796 (82.559)\tETA 0:22:39\tLoss 0.5688 (0.6215)\n",
      "Epoch: [0][40/792]\tTime 1.754 (91.355)\tETA 0:21:59\tLoss 0.5502 (0.6143)\n",
      "Epoch: [0][45/792]\tTime 1.744 (100.183)\tETA 0:21:42\tLoss 0.5644 (0.6083)\n",
      "Epoch: [0][50/792]\tTime 1.772 (108.935)\tETA 0:21:54\tLoss 0.5585 (0.6034)\n",
      "Epoch: [0][55/792]\tTime 1.756 (117.954)\tETA 0:21:34\tLoss 0.5613 (0.5989)\n",
      "Epoch: [0][60/792]\tTime 1.754 (126.845)\tETA 0:21:23\tLoss 0.5575 (0.5954)\n",
      "Epoch: [0][65/792]\tTime 1.833 (135.709)\tETA 0:22:12\tLoss 0.5516 (0.5924)\n",
      "Epoch: [0][70/792]\tTime 1.777 (144.529)\tETA 0:21:23\tLoss 0.5563 (0.5896)\n",
      "Epoch: [0][75/792]\tTime 1.761 (153.319)\tETA 0:21:02\tLoss 0.5469 (0.5869)\n",
      "Epoch: [0][80/792]\tTime 1.746 (162.091)\tETA 0:20:43\tLoss 0.5600 (0.5848)\n",
      "Epoch: [0][85/792]\tTime 1.881 (171.069)\tETA 0:22:09\tLoss 0.5507 (0.5829)\n",
      "Epoch: [0][90/792]\tTime 2.165 (180.324)\tETA 0:25:19\tLoss 0.5496 (0.5812)\n",
      "Epoch: [0][95/792]\tTime 1.884 (189.564)\tETA 0:21:53\tLoss 0.5491 (0.5797)\n",
      "Epoch: [0][100/792]\tTime 1.749 (198.462)\tETA 0:20:10\tLoss 0.5460 (0.5784)\n",
      "Epoch: [0][105/792]\tTime 1.816 (207.465)\tETA 0:20:47\tLoss 0.5574 (0.5772)\n",
      "Epoch: [0][110/792]\tTime 1.767 (216.390)\tETA 0:20:05\tLoss 0.5533 (0.5760)\n",
      "Epoch: [0][115/792]\tTime 1.814 (225.149)\tETA 0:20:28\tLoss 0.5468 (0.5747)\n",
      "Epoch: [0][120/792]\tTime 1.750 (234.045)\tETA 0:19:35\tLoss 0.5510 (0.5736)\n",
      "Epoch: [0][125/792]\tTime 1.810 (243.179)\tETA 0:20:07\tLoss 0.5542 (0.5726)\n",
      "Epoch: [0][130/792]\tTime 1.778 (252.085)\tETA 0:19:36\tLoss 0.5609 (0.5719)\n",
      "Epoch: [0][135/792]\tTime 1.924 (261.197)\tETA 0:21:03\tLoss 0.5551 (0.5713)\n",
      "Epoch: [0][140/792]\tTime 1.841 (270.618)\tETA 0:20:00\tLoss 0.5592 (0.5706)\n",
      "Epoch: [0][145/792]\tTime 1.764 (279.450)\tETA 0:19:01\tLoss 0.5578 (0.5700)\n",
      "Epoch: [0][150/792]\tTime 1.742 (288.421)\tETA 0:18:38\tLoss 0.5565 (0.5695)\n",
      "Epoch: [0][155/792]\tTime 1.806 (297.322)\tETA 0:19:10\tLoss 0.5443 (0.5689)\n",
      "Epoch: [0][160/792]\tTime 1.807 (306.392)\tETA 0:19:02\tLoss 0.5567 (0.5683)\n",
      "Epoch: [0][165/792]\tTime 1.795 (315.102)\tETA 0:18:45\tLoss 0.5486 (0.5678)\n",
      "Epoch: [0][170/792]\tTime 1.720 (323.939)\tETA 0:17:50\tLoss 0.5513 (0.5673)\n",
      "Epoch: [0][175/792]\tTime 1.744 (332.837)\tETA 0:17:56\tLoss 0.5460 (0.5668)\n",
      "Epoch: [0][180/792]\tTime 1.803 (341.731)\tETA 0:18:23\tLoss 0.5509 (0.5663)\n",
      "Epoch: [0][185/792]\tTime 1.758 (350.604)\tETA 0:17:47\tLoss 0.5431 (0.5658)\n",
      "Epoch: [0][190/792]\tTime 1.773 (359.578)\tETA 0:17:47\tLoss 0.5527 (0.5654)\n",
      "Epoch: [0][195/792]\tTime 1.814 (368.651)\tETA 0:18:02\tLoss 0.5472 (0.5651)\n",
      "Epoch: [0][200/792]\tTime 1.732 (377.611)\tETA 0:17:05\tLoss 0.5521 (0.5647)\n",
      "Epoch: [0][205/792]\tTime 1.774 (386.407)\tETA 0:17:21\tLoss 0.5444 (0.5643)\n",
      "Epoch: [0][210/792]\tTime 1.819 (395.198)\tETA 0:17:38\tLoss 0.5527 (0.5641)\n",
      "Epoch: [0][215/792]\tTime 1.742 (404.032)\tETA 0:16:45\tLoss 0.5460 (0.5636)\n",
      "Epoch: [0][220/792]\tTime 1.796 (412.982)\tETA 0:17:07\tLoss 0.5571 (0.5634)\n",
      "Epoch: [0][225/792]\tTime 1.786 (421.900)\tETA 0:16:52\tLoss 0.5481 (0.5631)\n",
      "Epoch: [0][230/792]\tTime 1.835 (430.884)\tETA 0:17:11\tLoss 0.5471 (0.5628)\n",
      "Epoch: [0][235/792]\tTime 1.846 (439.898)\tETA 0:17:08\tLoss 0.5461 (0.5625)\n",
      "Epoch: [0][240/792]\tTime 1.931 (449.038)\tETA 0:17:46\tLoss 0.5485 (0.5622)\n",
      "Epoch: [0][245/792]\tTime 1.734 (458.046)\tETA 0:15:48\tLoss 0.5478 (0.5619)\n",
      "Epoch: [0][250/792]\tTime 1.833 (466.871)\tETA 0:16:33\tLoss 0.5488 (0.5617)\n",
      "Epoch: [0][255/792]\tTime 1.790 (475.803)\tETA 0:16:01\tLoss 0.5450 (0.5615)\n",
      "Epoch: [0][260/792]\tTime 1.713 (484.701)\tETA 0:15:11\tLoss 0.5490 (0.5612)\n",
      "Epoch: [0][265/792]\tTime 1.776 (493.693)\tETA 0:15:35\tLoss 0.5541 (0.5610)\n",
      "Epoch: [0][270/792]\tTime 1.879 (502.688)\tETA 0:16:21\tLoss 0.5462 (0.5607)\n",
      "Epoch: [0][275/792]\tTime 1.711 (511.729)\tETA 0:14:44\tLoss 0.5529 (0.5605)\n",
      "Epoch: [0][280/792]\tTime 1.778 (520.606)\tETA 0:15:10\tLoss 0.5551 (0.5602)\n",
      "Epoch: [0][285/792]\tTime 1.774 (529.479)\tETA 0:14:59\tLoss 0.5513 (0.5600)\n",
      "Epoch: [0][290/792]\tTime 1.864 (538.652)\tETA 0:15:35\tLoss 0.5505 (0.5598)\n",
      "Epoch: [0][295/792]\tTime 1.793 (547.710)\tETA 0:14:51\tLoss 0.5612 (0.5597)\n",
      "Epoch: [0][300/792]\tTime 1.757 (556.657)\tETA 0:14:24\tLoss 0.5493 (0.5595)\n",
      "Epoch: [0][305/792]\tTime 1.784 (565.563)\tETA 0:14:28\tLoss 0.5417 (0.5593)\n",
      "Epoch: [0][310/792]\tTime 1.805 (574.489)\tETA 0:14:30\tLoss 0.5473 (0.5591)\n",
      "Epoch: [0][315/792]\tTime 1.883 (583.388)\tETA 0:14:58\tLoss 0.5454 (0.5589)\n",
      "Epoch: [0][320/792]\tTime 1.785 (592.445)\tETA 0:14:02\tLoss 0.5506 (0.5588)\n",
      "Epoch: [0][325/792]\tTime 1.720 (601.309)\tETA 0:13:23\tLoss 0.5494 (0.5587)\n",
      "Epoch: [0][330/792]\tTime 1.881 (610.810)\tETA 0:14:28\tLoss 0.5504 (0.5586)\n",
      "Epoch: [0][335/792]\tTime 1.922 (620.023)\tETA 0:14:38\tLoss 0.5476 (0.5586)\n",
      "Epoch: [0][340/792]\tTime 1.779 (629.231)\tETA 0:13:24\tLoss 0.5472 (0.5584)\n",
      "Epoch: [0][345/792]\tTime 1.767 (638.128)\tETA 0:13:10\tLoss 0.5488 (0.5583)\n",
      "Epoch: [0][350/792]\tTime 1.785 (647.066)\tETA 0:13:09\tLoss 0.5428 (0.5581)\n",
      "Epoch: [0][355/792]\tTime 1.755 (655.835)\tETA 0:12:46\tLoss 0.5467 (0.5580)\n",
      "Epoch: [0][360/792]\tTime 1.800 (664.699)\tETA 0:12:57\tLoss 0.5462 (0.5578)\n",
      "Epoch: [0][365/792]\tTime 1.938 (673.743)\tETA 0:13:47\tLoss 0.5520 (0.5577)\n",
      "Epoch: [0][370/792]\tTime 1.757 (682.716)\tETA 0:12:21\tLoss 0.5469 (0.5576)\n",
      "Epoch: [0][375/792]\tTime 1.788 (691.573)\tETA 0:12:25\tLoss 0.5401 (0.5574)\n",
      "Epoch: [0][380/792]\tTime 1.765 (700.500)\tETA 0:12:07\tLoss 0.5478 (0.5573)\n",
      "Epoch: [0][385/792]\tTime 1.789 (709.323)\tETA 0:12:08\tLoss 0.5433 (0.5571)\n",
      "Epoch: [0][390/792]\tTime 1.835 (718.458)\tETA 0:12:17\tLoss 0.5503 (0.5570)\n",
      "Epoch: [0][395/792]\tTime 1.779 (727.415)\tETA 0:11:46\tLoss 0.5443 (0.5569)\n",
      "Epoch: [0][400/792]\tTime 1.780 (736.305)\tETA 0:11:37\tLoss 0.5490 (0.5567)\n",
      "Epoch: [0][405/792]\tTime 1.831 (745.333)\tETA 0:11:48\tLoss 0.5438 (0.5566)\n",
      "Epoch: [0][410/792]\tTime 1.743 (754.297)\tETA 0:11:05\tLoss 0.5433 (0.5564)\n",
      "Epoch: [0][415/792]\tTime 1.741 (763.002)\tETA 0:10:56\tLoss 0.5404 (0.5563)\n",
      "Epoch: [0][420/792]\tTime 1.781 (772.042)\tETA 0:11:02\tLoss 0.5534 (0.5562)\n",
      "Epoch: [0][425/792]\tTime 1.766 (780.879)\tETA 0:10:48\tLoss 0.5503 (0.5562)\n",
      "Epoch: [0][430/792]\tTime 1.729 (789.598)\tETA 0:10:25\tLoss 0.5466 (0.5561)\n",
      "Epoch: [0][435/792]\tTime 1.789 (798.525)\tETA 0:10:38\tLoss 0.5488 (0.5560)\n",
      "Epoch: [0][440/792]\tTime 1.776 (807.469)\tETA 0:10:25\tLoss 0.5488 (0.5559)\n",
      "Epoch: [0][445/792]\tTime 1.849 (816.482)\tETA 0:10:41\tLoss 0.5491 (0.5558)\n",
      "Epoch: [0][450/792]\tTime 1.787 (825.337)\tETA 0:10:10\tLoss 0.5675 (0.5558)\n",
      "Epoch: [0][455/792]\tTime 1.765 (834.386)\tETA 0:09:54\tLoss 0.5476 (0.5557)\n",
      "Epoch: [0][460/792]\tTime 1.683 (843.175)\tETA 0:09:18\tLoss 0.5444 (0.5556)\n",
      "Epoch: [0][465/792]\tTime 1.759 (851.933)\tETA 0:09:35\tLoss 0.5436 (0.5555)\n",
      "Epoch: [0][470/792]\tTime 1.766 (860.956)\tETA 0:09:28\tLoss 0.5411 (0.5554)\n",
      "Epoch: [0][475/792]\tTime 1.718 (869.732)\tETA 0:09:04\tLoss 0.5578 (0.5554)\n",
      "Epoch: [0][480/792]\tTime 1.701 (878.461)\tETA 0:08:50\tLoss 0.5432 (0.5553)\n",
      "Epoch: [0][485/792]\tTime 1.763 (887.333)\tETA 0:09:01\tLoss 0.5441 (0.5552)\n",
      "Epoch: [0][490/792]\tTime 1.800 (896.374)\tETA 0:09:03\tLoss 0.5452 (0.5551)\n",
      "Epoch: [0][495/792]\tTime 1.772 (905.255)\tETA 0:08:46\tLoss 0.5469 (0.5551)\n",
      "Epoch: [0][500/792]\tTime 1.819 (914.219)\tETA 0:08:51\tLoss 0.5458 (0.5550)\n",
      "Epoch: [0][505/792]\tTime 1.834 (923.373)\tETA 0:08:46\tLoss 0.5448 (0.5549)\n",
      "Epoch: [0][510/792]\tTime 1.832 (932.342)\tETA 0:08:36\tLoss 0.5498 (0.5548)\n",
      "Epoch: [0][515/792]\tTime 1.818 (941.257)\tETA 0:08:23\tLoss 0.5473 (0.5547)\n",
      "Epoch: [0][520/792]\tTime 1.849 (950.285)\tETA 0:08:22\tLoss 0.5441 (0.5546)\n",
      "Epoch: [0][525/792]\tTime 1.760 (959.230)\tETA 0:07:50\tLoss 0.5442 (0.5546)\n",
      "Epoch: [0][530/792]\tTime 1.764 (968.347)\tETA 0:07:42\tLoss 0.5454 (0.5545)\n",
      "Epoch: [0][535/792]\tTime 1.886 (977.639)\tETA 0:08:04\tLoss 0.5427 (0.5544)\n",
      "Epoch: [0][540/792]\tTime 1.853 (987.037)\tETA 0:07:46\tLoss 0.5444 (0.5543)\n",
      "Epoch: [0][545/792]\tTime 1.976 (996.281)\tETA 0:08:07\tLoss 0.5456 (0.5542)\n",
      "Epoch: [0][550/792]\tTime 1.757 (1005.168)\tETA 0:07:05\tLoss 0.5465 (0.5541)\n",
      "Epoch: [0][555/792]\tTime 1.905 (1014.797)\tETA 0:07:31\tLoss 0.5468 (0.5540)\n",
      "Epoch: [0][560/792]\tTime 1.830 (1023.918)\tETA 0:07:04\tLoss 0.5437 (0.5540)\n",
      "Epoch: [0][565/792]\tTime 1.807 (1033.033)\tETA 0:06:50\tLoss 0.5426 (0.5539)\n",
      "Epoch: [0][570/792]\tTime 1.765 (1041.905)\tETA 0:06:31\tLoss 0.5551 (0.5538)\n",
      "Epoch: [0][575/792]\tTime 1.802 (1050.965)\tETA 0:06:30\tLoss 0.5417 (0.5537)\n",
      "Epoch: [0][580/792]\tTime 1.755 (1059.896)\tETA 0:06:12\tLoss 0.5415 (0.5537)\n",
      "Epoch: [0][585/792]\tTime 1.855 (1068.985)\tETA 0:06:24\tLoss 0.5459 (0.5536)\n",
      "Epoch: [0][590/792]\tTime 1.764 (1077.912)\tETA 0:05:56\tLoss 0.5439 (0.5535)\n",
      "Epoch: [0][595/792]\tTime 1.811 (1086.874)\tETA 0:05:56\tLoss 0.5505 (0.5535)\n",
      "Epoch: [0][600/792]\tTime 1.728 (1095.649)\tETA 0:05:31\tLoss 0.5425 (0.5534)\n",
      "Epoch: [0][605/792]\tTime 1.793 (1104.573)\tETA 0:05:35\tLoss 0.5412 (0.5533)\n",
      "Epoch: [0][610/792]\tTime 1.707 (1113.288)\tETA 0:05:10\tLoss 0.5511 (0.5532)\n",
      "Epoch: [0][615/792]\tTime 1.802 (1122.421)\tETA 0:05:18\tLoss 0.5486 (0.5532)\n",
      "Epoch: [0][620/792]\tTime 1.768 (1131.191)\tETA 0:05:04\tLoss 0.5583 (0.5531)\n",
      "Epoch: [0][625/792]\tTime 1.884 (1140.201)\tETA 0:05:14\tLoss 0.5383 (0.5531)\n",
      "Epoch: [0][630/792]\tTime 1.744 (1149.053)\tETA 0:04:42\tLoss 0.5507 (0.5530)\n",
      "Epoch: [0][635/792]\tTime 1.749 (1157.981)\tETA 0:04:34\tLoss 0.5463 (0.5530)\n",
      "Epoch: [0][640/792]\tTime 1.939 (1167.258)\tETA 0:04:54\tLoss 0.5441 (0.5529)\n",
      "Epoch: [0][645/792]\tTime 1.770 (1176.313)\tETA 0:04:20\tLoss 0.5468 (0.5528)\n",
      "Epoch: [0][650/792]\tTime 1.812 (1185.099)\tETA 0:04:17\tLoss 0.5442 (0.5527)\n",
      "Epoch: [0][655/792]\tTime 1.757 (1193.904)\tETA 0:04:00\tLoss 0.5483 (0.5527)\n",
      "Epoch: [0][660/792]\tTime 1.751 (1202.882)\tETA 0:03:51\tLoss 0.5461 (0.5526)\n",
      "Epoch: [0][665/792]\tTime 1.764 (1211.794)\tETA 0:03:43\tLoss 0.5440 (0.5526)\n",
      "Epoch: [0][670/792]\tTime 1.743 (1220.655)\tETA 0:03:32\tLoss 0.5464 (0.5525)\n",
      "Epoch: [0][675/792]\tTime 1.901 (1229.737)\tETA 0:03:42\tLoss 0.5429 (0.5525)\n",
      "Epoch: [0][680/792]\tTime 1.883 (1238.875)\tETA 0:03:30\tLoss 0.5526 (0.5524)\n",
      "Epoch: [0][685/792]\tTime 1.793 (1248.025)\tETA 0:03:11\tLoss 0.5495 (0.5524)\n",
      "Epoch: [0][690/792]\tTime 1.724 (1256.853)\tETA 0:02:55\tLoss 0.5443 (0.5523)\n",
      "Epoch: [0][695/792]\tTime 1.750 (1265.745)\tETA 0:02:49\tLoss 0.5543 (0.5524)\n",
      "Epoch: [0][700/792]\tTime 1.829 (1274.663)\tETA 0:02:48\tLoss 0.5522 (0.5524)\n",
      "Epoch: [0][705/792]\tTime 1.710 (1283.468)\tETA 0:02:28\tLoss 0.5593 (0.5524)\n",
      "Epoch: [0][710/792]\tTime 1.744 (1292.216)\tETA 0:02:23\tLoss 0.5462 (0.5523)\n",
      "Epoch: [0][715/792]\tTime 1.761 (1301.172)\tETA 0:02:15\tLoss 0.5438 (0.5523)\n",
      "Epoch: [0][720/792]\tTime 1.776 (1310.422)\tETA 0:02:07\tLoss 0.5470 (0.5522)\n",
      "Epoch: [0][725/792]\tTime 1.710 (1319.179)\tETA 0:01:54\tLoss 0.5465 (0.5522)\n",
      "Epoch: [0][730/792]\tTime 1.749 (1327.940)\tETA 0:01:48\tLoss 0.5421 (0.5522)\n",
      "Epoch: [0][735/792]\tTime 1.804 (1336.793)\tETA 0:01:42\tLoss 0.5483 (0.5521)\n",
      "Epoch: [0][740/792]\tTime 1.772 (1345.863)\tETA 0:01:32\tLoss 0.5489 (0.5520)\n",
      "Epoch: [0][745/792]\tTime 1.823 (1354.788)\tETA 0:01:25\tLoss 0.5478 (0.5520)\n",
      "Epoch: [0][750/792]\tTime 1.814 (1363.788)\tETA 0:01:16\tLoss 0.5455 (0.5520)\n",
      "Epoch: [0][755/792]\tTime 1.745 (1372.657)\tETA 0:01:04\tLoss 0.5554 (0.5520)\n",
      "Epoch: [0][760/792]\tTime 1.739 (1381.488)\tETA 0:00:55\tLoss 0.5532 (0.5520)\n",
      "Epoch: [0][765/792]\tTime 1.769 (1390.457)\tETA 0:00:47\tLoss 0.5528 (0.5520)\n",
      "Epoch: [0][770/792]\tTime 1.771 (1399.370)\tETA 0:00:38\tLoss 0.5442 (0.5519)\n",
      "Epoch: [0][775/792]\tTime 1.784 (1408.436)\tETA 0:00:30\tLoss 0.5441 (0.5519)\n",
      "Epoch: [0][780/792]\tTime 1.773 (1417.289)\tETA 0:00:21\tLoss 0.5441 (0.5518)\n",
      "Epoch: [0][785/792]\tTime 1.760 (1426.155)\tETA 0:00:12\tLoss 0.5419 (0.5517)\n",
      "Epoch: [0][790/792]\tTime 1.836 (1435.085)\tETA 0:00:03\tLoss 0.5424 (0.5517)\n",
      "Epoch: [1][0/792]\tTime 1.758 (1.758)\tETA 0:23:12\tLoss 0.5416 (0.5416)\n",
      "Epoch: [1][5/792]\tTime 1.738 (10.427)\tETA 0:22:47\tLoss 0.5541 (0.5491)\n",
      "Epoch: [1][10/792]\tTime 1.788 (19.461)\tETA 0:23:18\tLoss 0.5498 (0.5492)\n",
      "Epoch: [1][15/792]\tTime 1.758 (28.449)\tETA 0:22:45\tLoss 0.5446 (0.5473)\n",
      "Epoch: [1][20/792]\tTime 1.747 (37.486)\tETA 0:22:28\tLoss 0.5436 (0.5463)\n",
      "Epoch: [1][25/792]\tTime 1.795 (46.547)\tETA 0:22:56\tLoss 0.5437 (0.5460)\n",
      "Epoch: [1][30/792]\tTime 1.766 (55.469)\tETA 0:22:25\tLoss 0.5430 (0.5455)\n",
      "Epoch: [1][35/792]\tTime 1.800 (64.420)\tETA 0:22:42\tLoss 0.5512 (0.5453)\n",
      "Epoch: [1][40/792]\tTime 1.729 (73.191)\tETA 0:21:40\tLoss 0.5496 (0.5456)\n",
      "Epoch: [1][45/792]\tTime 1.743 (82.007)\tETA 0:21:42\tLoss 0.5486 (0.5466)\n",
      "Epoch: [1][50/792]\tTime 1.780 (90.944)\tETA 0:22:00\tLoss 0.5421 (0.5472)\n",
      "Epoch: [1][55/792]\tTime 1.851 (99.867)\tETA 0:22:44\tLoss 0.5502 (0.5474)\n",
      "Epoch: [1][60/792]\tTime 1.788 (108.767)\tETA 0:21:48\tLoss 0.5485 (0.5473)\n",
      "Epoch: [1][65/792]\tTime 1.731 (117.567)\tETA 0:20:58\tLoss 0.5479 (0.5474)\n",
      "Epoch: [1][70/792]\tTime 1.716 (126.315)\tETA 0:20:39\tLoss 0.5458 (0.5472)\n",
      "Epoch: [1][75/792]\tTime 1.714 (135.263)\tETA 0:20:28\tLoss 0.5463 (0.5469)\n",
      "Epoch: [1][80/792]\tTime 1.848 (144.582)\tETA 0:21:55\tLoss 0.5431 (0.5467)\n",
      "Epoch: [1][85/792]\tTime 1.844 (153.567)\tETA 0:21:43\tLoss 0.5524 (0.5466)\n",
      "Epoch: [1][90/792]\tTime 1.757 (163.160)\tETA 0:20:33\tLoss 0.5409 (0.5466)\n",
      "Epoch: [1][95/792]\tTime 1.756 (172.040)\tETA 0:20:24\tLoss 0.5423 (0.5465)\n",
      "Epoch: [1][100/792]\tTime 1.799 (181.164)\tETA 0:20:45\tLoss 0.5471 (0.5463)\n",
      "Epoch: [1][105/792]\tTime 1.823 (190.071)\tETA 0:20:52\tLoss 0.5458 (0.5463)\n",
      "Epoch: [1][110/792]\tTime 1.790 (199.187)\tETA 0:20:20\tLoss 0.5444 (0.5462)\n",
      "Epoch: [1][115/792]\tTime 1.928 (208.124)\tETA 0:21:45\tLoss 0.5413 (0.5461)\n",
      "Epoch: [1][120/792]\tTime 1.821 (217.119)\tETA 0:20:23\tLoss 0.5485 (0.5460)\n",
      "Epoch: [1][125/792]\tTime 1.752 (226.149)\tETA 0:19:28\tLoss 0.5471 (0.5459)\n",
      "Epoch: [1][130/792]\tTime 1.783 (235.128)\tETA 0:19:40\tLoss 0.5388 (0.5459)\n",
      "Epoch: [1][135/792]\tTime 1.785 (244.029)\tETA 0:19:32\tLoss 0.5443 (0.5458)\n",
      "Epoch: [1][140/792]\tTime 1.750 (252.856)\tETA 0:19:00\tLoss 0.5514 (0.5458)\n",
      "Epoch: [1][145/792]\tTime 1.755 (262.036)\tETA 0:18:55\tLoss 0.5469 (0.5459)\n",
      "Epoch: [1][150/792]\tTime 1.784 (270.785)\tETA 0:19:05\tLoss 0.5461 (0.5459)\n",
      "Epoch: [1][155/792]\tTime 1.707 (279.808)\tETA 0:18:07\tLoss 0.5441 (0.5457)\n",
      "Epoch: [1][160/792]\tTime 2.070 (289.103)\tETA 0:21:48\tLoss 0.5376 (0.5456)\n",
      "Epoch: [1][165/792]\tTime 1.767 (297.912)\tETA 0:18:28\tLoss 0.5435 (0.5455)\n",
      "Epoch: [1][170/792]\tTime 1.768 (306.928)\tETA 0:18:19\tLoss 0.5379 (0.5454)\n",
      "Epoch: [1][175/792]\tTime 1.754 (315.711)\tETA 0:18:02\tLoss 0.5461 (0.5454)\n",
      "Epoch: [1][180/792]\tTime 1.843 (324.668)\tETA 0:18:48\tLoss 0.5391 (0.5453)\n",
      "Epoch: [1][185/792]\tTime 2.160 (334.052)\tETA 0:21:51\tLoss 0.5453 (0.5452)\n",
      "Epoch: [1][190/792]\tTime 1.793 (343.232)\tETA 0:17:59\tLoss 0.5422 (0.5452)\n",
      "Epoch: [1][195/792]\tTime 1.796 (352.074)\tETA 0:17:51\tLoss 0.5451 (0.5451)\n",
      "Epoch: [1][200/792]\tTime 1.716 (361.155)\tETA 0:16:55\tLoss 0.5434 (0.5451)\n",
      "Epoch: [1][205/792]\tTime 1.850 (370.152)\tETA 0:18:05\tLoss 0.5442 (0.5450)\n",
      "Epoch: [1][210/792]\tTime 1.893 (379.251)\tETA 0:18:21\tLoss 0.5384 (0.5450)\n",
      "Epoch: [1][215/792]\tTime 1.746 (388.166)\tETA 0:16:47\tLoss 0.5420 (0.5450)\n",
      "Epoch: [1][220/792]\tTime 1.810 (397.045)\tETA 0:17:15\tLoss 0.5442 (0.5449)\n",
      "Epoch: [1][225/792]\tTime 1.773 (405.880)\tETA 0:16:45\tLoss 0.5420 (0.5448)\n",
      "Epoch: [1][230/792]\tTime 1.693 (414.600)\tETA 0:15:51\tLoss 0.5404 (0.5447)\n",
      "Epoch: [1][235/792]\tTime 1.718 (423.382)\tETA 0:15:56\tLoss 0.5429 (0.5447)\n",
      "Epoch: [1][240/792]\tTime 1.809 (432.380)\tETA 0:16:38\tLoss 0.5383 (0.5446)\n",
      "Epoch: [1][245/792]\tTime 1.856 (441.937)\tETA 0:16:55\tLoss 0.5410 (0.5445)\n",
      "Epoch: [1][250/792]\tTime 1.818 (450.954)\tETA 0:16:25\tLoss 0.5401 (0.5444)\n",
      "Epoch: [1][255/792]\tTime 1.827 (459.944)\tETA 0:16:20\tLoss 0.5364 (0.5444)\n",
      "Epoch: [1][260/792]\tTime 1.776 (468.864)\tETA 0:15:44\tLoss 0.5463 (0.5444)\n",
      "Epoch: [1][265/792]\tTime 1.887 (478.025)\tETA 0:16:34\tLoss 0.5403 (0.5444)\n",
      "Epoch: [1][270/792]\tTime 1.761 (486.932)\tETA 0:15:19\tLoss 0.5412 (0.5444)\n",
      "Epoch: [1][275/792]\tTime 1.759 (495.814)\tETA 0:15:09\tLoss 0.5405 (0.5444)\n",
      "Epoch: [1][280/792]\tTime 1.781 (504.787)\tETA 0:15:11\tLoss 0.5538 (0.5444)\n",
      "Epoch: [1][285/792]\tTime 1.695 (513.591)\tETA 0:14:19\tLoss 0.5420 (0.5445)\n",
      "Epoch: [1][290/792]\tTime 1.765 (522.400)\tETA 0:14:46\tLoss 0.5454 (0.5444)\n",
      "Epoch: [1][295/792]\tTime 1.794 (531.207)\tETA 0:14:51\tLoss 0.5394 (0.5443)\n",
      "Epoch: [1][300/792]\tTime 1.792 (540.040)\tETA 0:14:41\tLoss 0.5379 (0.5443)\n",
      "Epoch: [1][305/792]\tTime 1.824 (549.079)\tETA 0:14:48\tLoss 0.5492 (0.5443)\n",
      "Epoch: [1][310/792]\tTime 1.704 (558.221)\tETA 0:13:41\tLoss 0.5473 (0.5443)\n",
      "Epoch: [1][315/792]\tTime 1.784 (567.313)\tETA 0:14:10\tLoss 0.5448 (0.5443)\n",
      "Epoch: [1][320/792]\tTime 1.775 (576.246)\tETA 0:13:57\tLoss 0.5380 (0.5443)\n",
      "Epoch: [1][325/792]\tTime 1.947 (585.430)\tETA 0:15:09\tLoss 0.5408 (0.5442)\n",
      "Epoch: [1][330/792]\tTime 1.727 (594.478)\tETA 0:13:18\tLoss 0.5462 (0.5442)\n",
      "Epoch: [1][335/792]\tTime 1.801 (603.584)\tETA 0:13:43\tLoss 0.5466 (0.5442)\n",
      "Epoch: [1][340/792]\tTime 1.769 (612.536)\tETA 0:13:19\tLoss 0.5428 (0.5441)\n",
      "Epoch: [1][345/792]\tTime 1.793 (621.915)\tETA 0:13:21\tLoss 0.5363 (0.5440)\n",
      "Epoch: [1][350/792]\tTime 1.844 (631.205)\tETA 0:13:35\tLoss 0.5463 (0.5440)\n",
      "Epoch: [1][355/792]\tTime 1.796 (640.021)\tETA 0:13:04\tLoss 0.5422 (0.5440)\n",
      "Epoch: [1][360/792]\tTime 1.781 (648.955)\tETA 0:12:49\tLoss 0.5394 (0.5440)\n",
      "Epoch: [1][365/792]\tTime 1.806 (658.110)\tETA 0:12:51\tLoss 0.5423 (0.5439)\n",
      "Epoch: [1][370/792]\tTime 1.824 (667.057)\tETA 0:12:49\tLoss 0.5421 (0.5439)\n",
      "Epoch: [1][375/792]\tTime 1.794 (675.917)\tETA 0:12:28\tLoss 0.5408 (0.5438)\n",
      "Epoch: [1][380/792]\tTime 1.823 (685.220)\tETA 0:12:30\tLoss 0.5464 (0.5438)\n",
      "Epoch: [1][385/792]\tTime 1.790 (694.216)\tETA 0:12:08\tLoss 0.5445 (0.5438)\n",
      "Epoch: [1][390/792]\tTime 1.858 (703.422)\tETA 0:12:27\tLoss 0.5393 (0.5438)\n",
      "Epoch: [1][395/792]\tTime 1.830 (712.477)\tETA 0:12:06\tLoss 0.5425 (0.5438)\n",
      "Epoch: [1][400/792]\tTime 1.764 (721.487)\tETA 0:11:31\tLoss 0.5407 (0.5438)\n",
      "Epoch: [1][405/792]\tTime 1.766 (730.586)\tETA 0:11:23\tLoss 0.5435 (0.5438)\n",
      "Epoch: [1][410/792]\tTime 1.782 (739.907)\tETA 0:11:20\tLoss 0.5405 (0.5437)\n",
      "Epoch: [1][415/792]\tTime 1.746 (749.105)\tETA 0:10:58\tLoss 0.5452 (0.5438)\n",
      "Epoch: [1][420/792]\tTime 1.944 (758.205)\tETA 0:12:03\tLoss 0.5399 (0.5437)\n",
      "Epoch: [1][425/792]\tTime 1.827 (767.273)\tETA 0:11:10\tLoss 0.5545 (0.5438)\n",
      "Epoch: [1][430/792]\tTime 1.682 (775.996)\tETA 0:10:08\tLoss 0.5400 (0.5437)\n",
      "Epoch: [1][435/792]\tTime 1.798 (784.907)\tETA 0:10:41\tLoss 0.5441 (0.5437)\n",
      "Epoch: [1][440/792]\tTime 1.836 (793.798)\tETA 0:10:46\tLoss 0.5420 (0.5437)\n",
      "Epoch: [1][445/792]\tTime 1.780 (802.711)\tETA 0:10:17\tLoss 0.5397 (0.5437)\n",
      "Epoch: [1][450/792]\tTime 1.899 (812.004)\tETA 0:10:49\tLoss 0.5479 (0.5437)\n",
      "Epoch: [1][455/792]\tTime 1.777 (820.976)\tETA 0:09:58\tLoss 0.5447 (0.5437)\n",
      "Epoch: [1][460/792]\tTime 1.759 (829.952)\tETA 0:09:43\tLoss 0.5416 (0.5437)\n",
      "Epoch: [1][465/792]\tTime 1.875 (838.968)\tETA 0:10:13\tLoss 0.5431 (0.5437)\n",
      "Epoch: [1][470/792]\tTime 1.741 (847.711)\tETA 0:09:20\tLoss 0.5429 (0.5437)\n",
      "Epoch: [1][475/792]\tTime 1.831 (856.744)\tETA 0:09:40\tLoss 0.5469 (0.5437)\n",
      "Epoch: [1][480/792]\tTime 1.727 (865.552)\tETA 0:08:58\tLoss 0.5416 (0.5437)\n",
      "Epoch: [1][485/792]\tTime 1.773 (874.449)\tETA 0:09:04\tLoss 0.5433 (0.5436)\n",
      "Epoch: [1][490/792]\tTime 1.809 (883.417)\tETA 0:09:06\tLoss 0.5405 (0.5436)\n",
      "Epoch: [1][495/792]\tTime 1.784 (892.315)\tETA 0:08:49\tLoss 0.5397 (0.5436)\n",
      "Epoch: [1][500/792]\tTime 1.749 (901.174)\tETA 0:08:30\tLoss 0.5375 (0.5435)\n",
      "Epoch: [1][505/792]\tTime 1.849 (910.085)\tETA 0:08:50\tLoss 0.5390 (0.5435)\n",
      "Epoch: [1][510/792]\tTime 1.753 (918.896)\tETA 0:08:14\tLoss 0.5376 (0.5435)\n",
      "Epoch: [1][515/792]\tTime 1.817 (927.730)\tETA 0:08:23\tLoss 0.5427 (0.5434)\n",
      "Epoch: [1][520/792]\tTime 1.722 (936.707)\tETA 0:07:48\tLoss 0.5383 (0.5434)\n",
      "Epoch: [1][525/792]\tTime 1.782 (945.585)\tETA 0:07:55\tLoss 0.5377 (0.5434)\n",
      "Epoch: [1][530/792]\tTime 1.688 (954.410)\tETA 0:07:22\tLoss 0.5354 (0.5433)\n",
      "Epoch: [1][535/792]\tTime 1.847 (963.386)\tETA 0:07:54\tLoss 0.5417 (0.5433)\n",
      "Epoch: [1][540/792]\tTime 1.823 (972.470)\tETA 0:07:39\tLoss 0.5423 (0.5433)\n",
      "Epoch: [1][545/792]\tTime 1.829 (981.343)\tETA 0:07:31\tLoss 0.5385 (0.5432)\n",
      "Epoch: [1][550/792]\tTime 1.752 (990.695)\tETA 0:07:03\tLoss 0.5394 (0.5432)\n",
      "Epoch: [1][555/792]\tTime 1.729 (999.656)\tETA 0:06:49\tLoss 0.5384 (0.5432)\n",
      "Epoch: [1][560/792]\tTime 1.775 (1008.519)\tETA 0:06:51\tLoss 0.5486 (0.5432)\n",
      "Epoch: [1][565/792]\tTime 1.861 (1017.618)\tETA 0:07:02\tLoss 0.5371 (0.5431)\n",
      "Epoch: [1][570/792]\tTime 1.807 (1026.898)\tETA 0:06:41\tLoss 0.5376 (0.5431)\n",
      "Epoch: [1][575/792]\tTime 1.741 (1035.757)\tETA 0:06:17\tLoss 0.5427 (0.5431)\n",
      "Epoch: [1][580/792]\tTime 1.823 (1044.773)\tETA 0:06:26\tLoss 0.5435 (0.5431)\n",
      "Epoch: [1][585/792]\tTime 1.871 (1053.657)\tETA 0:06:27\tLoss 0.5395 (0.5431)\n",
      "Epoch: [1][590/792]\tTime 1.768 (1062.600)\tETA 0:05:57\tLoss 0.5421 (0.5431)\n",
      "Epoch: [1][595/792]\tTime 1.733 (1071.438)\tETA 0:05:41\tLoss 0.5404 (0.5431)\n",
      "Epoch: [1][600/792]\tTime 1.783 (1080.415)\tETA 0:05:42\tLoss 0.5385 (0.5431)\n",
      "Epoch: [1][605/792]\tTime 1.909 (1089.472)\tETA 0:05:56\tLoss 0.5409 (0.5430)\n",
      "Epoch: [1][610/792]\tTime 1.771 (1098.484)\tETA 0:05:22\tLoss 0.5396 (0.5430)\n",
      "Epoch: [1][615/792]\tTime 1.738 (1107.336)\tETA 0:05:07\tLoss 0.5377 (0.5430)\n",
      "Epoch: [1][620/792]\tTime 1.767 (1116.138)\tETA 0:05:03\tLoss 0.5395 (0.5430)\n",
      "Epoch: [1][625/792]\tTime 1.764 (1125.226)\tETA 0:04:54\tLoss 0.5362 (0.5429)\n",
      "Epoch: [1][630/792]\tTime 1.737 (1133.933)\tETA 0:04:41\tLoss 0.5374 (0.5429)\n",
      "Epoch: [1][635/792]\tTime 1.766 (1142.811)\tETA 0:04:37\tLoss 0.5374 (0.5429)\n",
      "Epoch: [1][640/792]\tTime 1.808 (1151.885)\tETA 0:04:34\tLoss 0.5371 (0.5428)\n",
      "Epoch: [1][645/792]\tTime 1.799 (1160.889)\tETA 0:04:24\tLoss 0.5392 (0.5428)\n",
      "Epoch: [1][650/792]\tTime 1.841 (1169.649)\tETA 0:04:21\tLoss 0.5398 (0.5428)\n",
      "Epoch: [1][655/792]\tTime 1.790 (1178.518)\tETA 0:04:05\tLoss 0.5367 (0.5427)\n",
      "Epoch: [1][660/792]\tTime 1.727 (1187.236)\tETA 0:03:47\tLoss 0.5381 (0.5427)\n",
      "Epoch: [1][665/792]\tTime 1.915 (1196.699)\tETA 0:04:03\tLoss 0.5383 (0.5427)\n",
      "Epoch: [1][670/792]\tTime 1.752 (1205.702)\tETA 0:03:33\tLoss 0.5377 (0.5426)\n",
      "Epoch: [1][675/792]\tTime 1.823 (1214.524)\tETA 0:03:33\tLoss 0.5392 (0.5426)\n",
      "Epoch: [1][680/792]\tTime 1.861 (1223.848)\tETA 0:03:28\tLoss 0.5422 (0.5426)\n",
      "Epoch: [1][685/792]\tTime 1.803 (1232.749)\tETA 0:03:12\tLoss 0.5429 (0.5426)\n",
      "Epoch: [1][690/792]\tTime 1.865 (1241.712)\tETA 0:03:10\tLoss 0.5460 (0.5426)\n",
      "Epoch: [1][695/792]\tTime 1.777 (1250.778)\tETA 0:02:52\tLoss 0.5390 (0.5426)\n",
      "Epoch: [1][700/792]\tTime 1.751 (1259.571)\tETA 0:02:41\tLoss 0.5400 (0.5426)\n",
      "Epoch: [1][705/792]\tTime 1.831 (1268.470)\tETA 0:02:39\tLoss 0.5427 (0.5426)\n",
      "Epoch: [1][710/792]\tTime 1.845 (1277.328)\tETA 0:02:31\tLoss 0.5416 (0.5426)\n",
      "Epoch: [1][715/792]\tTime 1.750 (1286.110)\tETA 0:02:14\tLoss 0.5398 (0.5425)\n",
      "Epoch: [1][720/792]\tTime 1.744 (1294.992)\tETA 0:02:05\tLoss 0.5421 (0.5425)\n",
      "Epoch: [1][725/792]\tTime 1.764 (1303.856)\tETA 0:01:58\tLoss 0.5393 (0.5425)\n",
      "Epoch: [1][730/792]\tTime 1.715 (1312.795)\tETA 0:01:46\tLoss 0.5493 (0.5425)\n",
      "Epoch: [1][735/792]\tTime 1.824 (1322.014)\tETA 0:01:43\tLoss 0.5375 (0.5425)\n",
      "Epoch: [1][740/792]\tTime 1.739 (1331.115)\tETA 0:01:30\tLoss 0.5420 (0.5424)\n",
      "Epoch: [1][745/792]\tTime 1.759 (1340.022)\tETA 0:01:22\tLoss 0.5373 (0.5424)\n",
      "Epoch: [1][750/792]\tTime 1.807 (1349.184)\tETA 0:01:15\tLoss 0.5440 (0.5424)\n",
      "Epoch: [1][755/792]\tTime 1.729 (1358.377)\tETA 0:01:03\tLoss 0.5361 (0.5424)\n",
      "Epoch: [1][760/792]\tTime 1.794 (1367.140)\tETA 0:00:57\tLoss 0.5394 (0.5424)\n",
      "Epoch: [1][765/792]\tTime 1.828 (1376.051)\tETA 0:00:49\tLoss 0.5425 (0.5423)\n",
      "Epoch: [1][770/792]\tTime 1.821 (1385.167)\tETA 0:00:40\tLoss 0.5351 (0.5423)\n",
      "Epoch: [1][775/792]\tTime 1.720 (1394.197)\tETA 0:00:29\tLoss 0.5409 (0.5423)\n",
      "Epoch: [1][780/792]\tTime 1.864 (1403.241)\tETA 0:00:22\tLoss 0.5442 (0.5423)\n",
      "Epoch: [1][785/792]\tTime 1.791 (1412.262)\tETA 0:00:12\tLoss 0.5365 (0.5423)\n",
      "Epoch: [1][790/792]\tTime 1.759 (1421.221)\tETA 0:00:03\tLoss 0.5416 (0.5423)\n",
      "Epoch: [2][0/792]\tTime 1.796 (1.796)\tETA 0:23:42\tLoss 0.5402 (0.5402)\n",
      "Epoch: [2][5/792]\tTime 1.842 (10.870)\tETA 0:24:09\tLoss 0.5401 (0.5439)\n",
      "Epoch: [2][10/792]\tTime 1.814 (19.889)\tETA 0:23:38\tLoss 0.5394 (0.5422)\n",
      "Epoch: [2][15/792]\tTime 1.762 (28.842)\tETA 0:22:49\tLoss 0.5403 (0.5410)\n",
      "Epoch: [2][20/792]\tTime 1.740 (37.744)\tETA 0:22:23\tLoss 0.5361 (0.5408)\n",
      "Epoch: [2][25/792]\tTime 1.788 (46.712)\tETA 0:22:51\tLoss 0.5386 (0.5402)\n",
      "Epoch: [2][30/792]\tTime 1.831 (56.132)\tETA 0:23:14\tLoss 0.5367 (0.5398)\n",
      "Epoch: [2][35/792]\tTime 1.821 (65.259)\tETA 0:22:58\tLoss 0.5339 (0.5395)\n",
      "Epoch: [2][40/792]\tTime 2.018 (74.348)\tETA 0:25:17\tLoss 0.5378 (0.5397)\n",
      "Epoch: [2][45/792]\tTime 1.734 (83.343)\tETA 0:21:35\tLoss 0.5558 (0.5400)\n",
      "Epoch: [2][50/792]\tTime 1.805 (92.197)\tETA 0:22:19\tLoss 0.5387 (0.5399)\n",
      "Epoch: [2][55/792]\tTime 1.770 (101.121)\tETA 0:21:44\tLoss 0.5400 (0.5399)\n",
      "Epoch: [2][60/792]\tTime 1.785 (110.285)\tETA 0:21:46\tLoss 0.5374 (0.5397)\n",
      "Epoch: [2][65/792]\tTime 1.816 (119.159)\tETA 0:22:00\tLoss 0.5403 (0.5398)\n",
      "Epoch: [2][70/792]\tTime 1.732 (128.238)\tETA 0:20:50\tLoss 0.5411 (0.5397)\n",
      "Epoch: [2][75/792]\tTime 1.773 (137.125)\tETA 0:21:11\tLoss 0.5441 (0.5399)\n",
      "Epoch: [2][80/792]\tTime 1.807 (145.998)\tETA 0:21:26\tLoss 0.5405 (0.5399)\n",
      "Epoch: [2][85/792]\tTime 1.776 (154.942)\tETA 0:20:55\tLoss 0.5368 (0.5400)\n",
      "Epoch: [2][90/792]\tTime 1.824 (164.088)\tETA 0:21:20\tLoss 0.5399 (0.5401)\n",
      "Epoch: [2][95/792]\tTime 1.774 (173.125)\tETA 0:20:36\tLoss 0.5433 (0.5404)\n",
      "Epoch: [2][100/792]\tTime 1.856 (182.147)\tETA 0:21:24\tLoss 0.5343 (0.5404)\n",
      "Epoch: [2][105/792]\tTime 1.751 (191.059)\tETA 0:20:03\tLoss 0.5435 (0.5405)\n",
      "Epoch: [2][110/792]\tTime 1.723 (199.940)\tETA 0:19:35\tLoss 0.5479 (0.5406)\n",
      "Epoch: [2][115/792]\tTime 1.839 (209.039)\tETA 0:20:45\tLoss 0.5500 (0.5408)\n",
      "Epoch: [2][120/792]\tTime 1.780 (217.957)\tETA 0:19:56\tLoss 0.5345 (0.5407)\n",
      "Epoch: [2][125/792]\tTime 1.752 (226.921)\tETA 0:19:28\tLoss 0.5437 (0.5406)\n",
      "Epoch: [2][130/792]\tTime 1.821 (236.083)\tETA 0:20:05\tLoss 0.5447 (0.5406)\n",
      "Epoch: [2][135/792]\tTime 1.805 (245.222)\tETA 0:19:45\tLoss 0.5346 (0.5406)\n",
      "Epoch: [2][140/792]\tTime 1.794 (254.190)\tETA 0:19:29\tLoss 0.5355 (0.5405)\n",
      "Epoch: [2][145/792]\tTime 1.733 (263.011)\tETA 0:18:41\tLoss 0.5424 (0.5405)\n",
      "Epoch: [2][150/792]\tTime 1.842 (272.074)\tETA 0:19:42\tLoss 0.5368 (0.5405)\n",
      "Epoch: [2][155/792]\tTime 1.815 (281.004)\tETA 0:19:16\tLoss 0.5417 (0.5404)\n",
      "Epoch: [2][160/792]\tTime 1.781 (290.024)\tETA 0:18:45\tLoss 0.5405 (0.5403)\n",
      "Epoch: [2][165/792]\tTime 1.788 (298.914)\tETA 0:18:41\tLoss 0.5354 (0.5403)\n",
      "Epoch: [2][170/792]\tTime 1.847 (308.041)\tETA 0:19:08\tLoss 0.5434 (0.5402)\n",
      "Epoch: [2][175/792]\tTime 1.771 (317.313)\tETA 0:18:12\tLoss 0.5348 (0.5402)\n",
      "Epoch: [2][180/792]\tTime 1.827 (326.934)\tETA 0:18:38\tLoss 0.5378 (0.5401)\n",
      "Epoch: [2][185/792]\tTime 1.777 (335.815)\tETA 0:17:58\tLoss 0.5366 (0.5402)\n",
      "Epoch: [2][190/792]\tTime 1.787 (344.713)\tETA 0:17:55\tLoss 0.5410 (0.5402)\n",
      "Epoch: [2][195/792]\tTime 1.861 (353.944)\tETA 0:18:31\tLoss 0.5386 (0.5401)\n",
      "Epoch: [2][200/792]\tTime 1.897 (363.188)\tETA 0:18:42\tLoss 0.5402 (0.5401)\n",
      "Epoch: [2][205/792]\tTime 1.752 (372.014)\tETA 0:17:08\tLoss 0.5339 (0.5400)\n",
      "Epoch: [2][210/792]\tTime 1.768 (380.813)\tETA 0:17:08\tLoss 0.5385 (0.5400)\n",
      "Epoch: [2][215/792]\tTime 1.803 (389.689)\tETA 0:17:20\tLoss 0.5369 (0.5401)\n",
      "Epoch: [2][220/792]\tTime 1.725 (398.975)\tETA 0:16:26\tLoss 0.5431 (0.5401)\n",
      "Epoch: [2][225/792]\tTime 1.835 (408.030)\tETA 0:17:20\tLoss 0.5378 (0.5400)\n",
      "Epoch: [2][230/792]\tTime 1.791 (417.017)\tETA 0:16:46\tLoss 0.5402 (0.5400)\n",
      "Epoch: [2][235/792]\tTime 1.734 (425.874)\tETA 0:16:05\tLoss 0.5427 (0.5400)\n",
      "Epoch: [2][240/792]\tTime 1.848 (434.855)\tETA 0:17:00\tLoss 0.5376 (0.5400)\n",
      "Epoch: [2][245/792]\tTime 1.815 (443.893)\tETA 0:16:32\tLoss 0.5405 (0.5400)\n",
      "Epoch: [2][250/792]\tTime 1.738 (452.723)\tETA 0:15:42\tLoss 0.5415 (0.5400)\n",
      "Epoch: [2][255/792]\tTime 1.798 (461.655)\tETA 0:16:05\tLoss 0.5372 (0.5400)\n",
      "Epoch: [2][260/792]\tTime 1.859 (470.737)\tETA 0:16:28\tLoss 0.5334 (0.5399)\n",
      "Epoch: [2][265/792]\tTime 1.795 (479.721)\tETA 0:15:45\tLoss 0.5415 (0.5399)\n",
      "Epoch: [2][270/792]\tTime 1.768 (488.662)\tETA 0:15:22\tLoss 0.5412 (0.5399)\n",
      "Epoch: [2][275/792]\tTime 2.059 (497.866)\tETA 0:17:44\tLoss 0.5338 (0.5399)\n",
      "Epoch: [2][280/792]\tTime 1.824 (507.036)\tETA 0:15:33\tLoss 0.5372 (0.5400)\n",
      "Epoch: [2][285/792]\tTime 1.766 (515.819)\tETA 0:14:55\tLoss 0.5425 (0.5400)\n",
      "Epoch: [2][290/792]\tTime 1.817 (524.693)\tETA 0:15:11\tLoss 0.5425 (0.5401)\n",
      "Epoch: [2][295/792]\tTime 1.822 (533.713)\tETA 0:15:05\tLoss 0.5424 (0.5402)\n",
      "Epoch: [2][300/792]\tTime 1.742 (542.469)\tETA 0:14:17\tLoss 0.5373 (0.5402)\n",
      "Epoch: [2][305/792]\tTime 1.762 (551.298)\tETA 0:14:18\tLoss 0.5416 (0.5402)\n",
      "Epoch: [2][310/792]\tTime 1.848 (560.262)\tETA 0:14:50\tLoss 0.5486 (0.5402)\n",
      "Epoch: [2][315/792]\tTime 1.772 (569.102)\tETA 0:14:05\tLoss 0.5415 (0.5403)\n",
      "Epoch: [2][320/792]\tTime 1.738 (578.406)\tETA 0:13:40\tLoss 0.5395 (0.5402)\n",
      "Epoch: [2][325/792]\tTime 1.826 (587.424)\tETA 0:14:12\tLoss 0.5399 (0.5402)\n",
      "Epoch: [2][330/792]\tTime 1.733 (596.409)\tETA 0:13:20\tLoss 0.5436 (0.5402)\n",
      "Epoch: [2][335/792]\tTime 1.905 (605.470)\tETA 0:14:30\tLoss 0.5406 (0.5402)\n",
      "Epoch: [2][340/792]\tTime 1.762 (614.762)\tETA 0:13:16\tLoss 0.5390 (0.5402)\n",
      "Epoch: [2][345/792]\tTime 1.802 (623.744)\tETA 0:13:25\tLoss 0.5361 (0.5402)\n",
      "Epoch: [2][350/792]\tTime 1.897 (632.763)\tETA 0:13:58\tLoss 0.5358 (0.5401)\n",
      "Epoch: [2][355/792]\tTime 1.812 (641.689)\tETA 0:13:11\tLoss 0.5370 (0.5401)\n",
      "Epoch: [2][360/792]\tTime 1.789 (650.591)\tETA 0:12:52\tLoss 0.5375 (0.5401)\n",
      "Epoch: [2][365/792]\tTime 1.846 (659.533)\tETA 0:13:08\tLoss 0.5394 (0.5401)\n",
      "Epoch: [2][370/792]\tTime 1.818 (668.605)\tETA 0:12:47\tLoss 0.5390 (0.5401)\n",
      "Epoch: [2][375/792]\tTime 1.830 (677.454)\tETA 0:12:43\tLoss 0.5360 (0.5400)\n",
      "Epoch: [2][380/792]\tTime 1.793 (686.489)\tETA 0:12:18\tLoss 0.5412 (0.5400)\n",
      "Epoch: [2][385/792]\tTime 2.179 (695.778)\tETA 0:14:47\tLoss 0.5374 (0.5400)\n",
      "Epoch: [2][390/792]\tTime 1.766 (704.706)\tETA 0:11:50\tLoss 0.5337 (0.5399)\n",
      "Epoch: [2][395/792]\tTime 1.783 (713.734)\tETA 0:11:47\tLoss 0.5453 (0.5399)\n",
      "Epoch: [2][400/792]\tTime 1.687 (722.705)\tETA 0:11:01\tLoss 0.5330 (0.5399)\n",
      "Epoch: [2][405/792]\tTime 1.762 (731.960)\tETA 0:11:22\tLoss 0.5356 (0.5398)\n",
      "Epoch: [2][410/792]\tTime 1.776 (741.273)\tETA 0:11:18\tLoss 0.5490 (0.5398)\n",
      "Epoch: [2][415/792]\tTime 1.721 (750.211)\tETA 0:10:48\tLoss 0.5361 (0.5398)\n",
      "Epoch: [2][420/792]\tTime 1.727 (759.054)\tETA 0:10:42\tLoss 0.5374 (0.5397)\n",
      "Epoch: [2][425/792]\tTime 1.821 (768.343)\tETA 0:11:08\tLoss 0.5446 (0.5397)\n",
      "Epoch: [2][430/792]\tTime 1.792 (777.417)\tETA 0:10:48\tLoss 0.5404 (0.5397)\n",
      "Epoch: [2][435/792]\tTime 1.735 (786.819)\tETA 0:10:19\tLoss 0.5356 (0.5397)\n",
      "Epoch: [2][440/792]\tTime 1.792 (795.915)\tETA 0:10:30\tLoss 0.5364 (0.5397)\n",
      "Epoch: [2][445/792]\tTime 1.750 (804.802)\tETA 0:10:07\tLoss 0.5418 (0.5397)\n",
      "Epoch: [2][450/792]\tTime 1.748 (813.802)\tETA 0:09:57\tLoss 0.5448 (0.5397)\n",
      "Epoch: [2][455/792]\tTime 1.884 (822.795)\tETA 0:10:34\tLoss 0.5402 (0.5397)\n",
      "Epoch: [2][460/792]\tTime 1.798 (832.095)\tETA 0:09:57\tLoss 0.5456 (0.5398)\n",
      "Epoch: [2][465/792]\tTime 1.760 (841.178)\tETA 0:09:35\tLoss 0.5374 (0.5398)\n",
      "Epoch: [2][470/792]\tTime 1.852 (850.165)\tETA 0:09:56\tLoss 0.5367 (0.5398)\n",
      "Epoch: [2][475/792]\tTime 1.696 (859.116)\tETA 0:08:57\tLoss 0.5359 (0.5397)\n",
      "Epoch: [2][480/792]\tTime 1.699 (867.837)\tETA 0:08:50\tLoss 0.5423 (0.5397)\n",
      "Epoch: [2][485/792]\tTime 1.871 (876.940)\tETA 0:09:34\tLoss 0.5360 (0.5397)\n",
      "Epoch: [2][490/792]\tTime 1.859 (886.088)\tETA 0:09:21\tLoss 0.5363 (0.5397)\n",
      "Epoch: [2][495/792]\tTime 1.781 (894.962)\tETA 0:08:48\tLoss 0.5337 (0.5396)\n",
      "Epoch: [2][500/792]\tTime 1.784 (903.800)\tETA 0:08:40\tLoss 0.5426 (0.5397)\n",
      "Epoch: [2][505/792]\tTime 1.714 (912.722)\tETA 0:08:11\tLoss 0.5325 (0.5396)\n",
      "Epoch: [2][510/792]\tTime 1.878 (922.781)\tETA 0:08:49\tLoss 0.5380 (0.5396)\n",
      "Epoch: [2][515/792]\tTime 1.914 (931.917)\tETA 0:08:50\tLoss 0.5369 (0.5396)\n",
      "Epoch: [2][520/792]\tTime 1.765 (940.906)\tETA 0:08:00\tLoss 0.5426 (0.5396)\n",
      "Epoch: [2][525/792]\tTime 1.860 (949.889)\tETA 0:08:16\tLoss 0.5397 (0.5396)\n",
      "Epoch: [2][530/792]\tTime 1.723 (958.958)\tETA 0:07:31\tLoss 0.5463 (0.5396)\n",
      "Epoch: [2][535/792]\tTime 1.782 (967.851)\tETA 0:07:37\tLoss 0.5440 (0.5396)\n",
      "Epoch: [2][540/792]\tTime 1.781 (976.763)\tETA 0:07:28\tLoss 0.5422 (0.5396)\n",
      "Epoch: [2][545/792]\tTime 1.798 (985.765)\tETA 0:07:24\tLoss 0.5387 (0.5396)\n",
      "Epoch: [2][550/792]\tTime 1.856 (994.717)\tETA 0:07:29\tLoss 0.5344 (0.5396)\n",
      "Epoch: [2][555/792]\tTime 1.840 (1003.697)\tETA 0:07:16\tLoss 0.5378 (0.5396)\n",
      "Epoch: [2][560/792]\tTime 1.794 (1012.714)\tETA 0:06:56\tLoss 0.5366 (0.5396)\n",
      "Epoch: [2][565/792]\tTime 1.753 (1021.509)\tETA 0:06:37\tLoss 0.5344 (0.5396)\n",
      "Epoch: [2][570/792]\tTime 1.801 (1030.621)\tETA 0:06:39\tLoss 0.5374 (0.5396)\n",
      "Epoch: [2][575/792]\tTime 1.818 (1039.404)\tETA 0:06:34\tLoss 0.5332 (0.5395)\n",
      "Epoch: [2][580/792]\tTime 1.781 (1048.495)\tETA 0:06:17\tLoss 0.5344 (0.5395)\n",
      "Epoch: [2][585/792]\tTime 1.830 (1057.889)\tETA 0:06:18\tLoss 0.5361 (0.5395)\n",
      "Epoch: [2][590/792]\tTime 1.922 (1066.955)\tETA 0:06:28\tLoss 0.5390 (0.5394)\n",
      "Epoch: [2][595/792]\tTime 1.756 (1075.976)\tETA 0:05:45\tLoss 0.5376 (0.5394)\n",
      "Epoch: [2][600/792]\tTime 1.735 (1084.769)\tETA 0:05:33\tLoss 0.5377 (0.5394)\n",
      "Epoch: [2][605/792]\tTime 1.727 (1094.363)\tETA 0:05:22\tLoss 0.5411 (0.5394)\n",
      "Epoch: [2][610/792]\tTime 1.728 (1103.160)\tETA 0:05:14\tLoss 0.5367 (0.5394)\n",
      "Epoch: [2][615/792]\tTime 1.854 (1112.502)\tETA 0:05:28\tLoss 0.5411 (0.5394)\n",
      "Epoch: [2][620/792]\tTime 1.807 (1121.552)\tETA 0:05:10\tLoss 0.5362 (0.5394)\n",
      "Epoch: [2][625/792]\tTime 1.714 (1130.278)\tETA 0:04:46\tLoss 0.5348 (0.5393)\n",
      "Epoch: [2][630/792]\tTime 1.853 (1139.606)\tETA 0:05:00\tLoss 0.5402 (0.5393)\n",
      "Epoch: [2][635/792]\tTime 1.800 (1148.455)\tETA 0:04:42\tLoss 0.5389 (0.5393)\n",
      "Epoch: [2][640/792]\tTime 1.784 (1157.330)\tETA 0:04:31\tLoss 0.5400 (0.5393)\n",
      "Epoch: [2][645/792]\tTime 1.843 (1166.579)\tETA 0:04:30\tLoss 0.5341 (0.5393)\n",
      "Epoch: [2][650/792]\tTime 1.781 (1175.611)\tETA 0:04:12\tLoss 0.5336 (0.5392)\n",
      "Epoch: [2][655/792]\tTime 1.746 (1184.639)\tETA 0:03:59\tLoss 0.5344 (0.5392)\n",
      "Epoch: [2][660/792]\tTime 1.896 (1193.551)\tETA 0:04:10\tLoss 0.5347 (0.5392)\n",
      "Epoch: [2][665/792]\tTime 1.863 (1203.003)\tETA 0:03:56\tLoss 0.5356 (0.5392)\n",
      "Epoch: [2][670/792]\tTime 1.737 (1212.102)\tETA 0:03:31\tLoss 0.5387 (0.5392)\n",
      "Epoch: [2][675/792]\tTime 1.875 (1221.104)\tETA 0:03:39\tLoss 0.5382 (0.5392)\n",
      "Epoch: [2][680/792]\tTime 1.759 (1230.157)\tETA 0:03:17\tLoss 0.5379 (0.5392)\n",
      "Epoch: [2][685/792]\tTime 1.700 (1239.215)\tETA 0:03:01\tLoss 0.5459 (0.5392)\n",
      "Epoch: [2][690/792]\tTime 1.695 (1248.174)\tETA 0:02:52\tLoss 0.5382 (0.5391)\n",
      "Epoch: [2][695/792]\tTime 1.790 (1256.946)\tETA 0:02:53\tLoss 0.5366 (0.5391)\n",
      "Epoch: [2][700/792]\tTime 1.744 (1266.025)\tETA 0:02:40\tLoss 0.5351 (0.5391)\n",
      "Epoch: [2][705/792]\tTime 1.765 (1274.918)\tETA 0:02:33\tLoss 0.5364 (0.5391)\n",
      "Epoch: [2][710/792]\tTime 1.760 (1283.886)\tETA 0:02:24\tLoss 0.5394 (0.5391)\n",
      "Epoch: [2][715/792]\tTime 1.750 (1292.775)\tETA 0:02:14\tLoss 0.5531 (0.5391)\n",
      "Epoch: [2][720/792]\tTime 1.776 (1301.630)\tETA 0:02:07\tLoss 0.5408 (0.5392)\n",
      "Epoch: [2][725/792]\tTime 1.734 (1310.573)\tETA 0:01:56\tLoss 0.5437 (0.5392)\n",
      "Epoch: [2][730/792]\tTime 1.750 (1319.473)\tETA 0:01:48\tLoss 0.5501 (0.5392)\n",
      "Epoch: [2][735/792]\tTime 1.803 (1328.189)\tETA 0:01:42\tLoss 0.5392 (0.5392)\n",
      "Epoch: [2][740/792]\tTime 1.789 (1337.157)\tETA 0:01:33\tLoss 0.5439 (0.5392)\n",
      "Epoch: [2][745/792]\tTime 1.778 (1345.913)\tETA 0:01:23\tLoss 0.5397 (0.5392)\n",
      "Epoch: [2][750/792]\tTime 1.778 (1354.731)\tETA 0:01:14\tLoss 0.5415 (0.5392)\n",
      "Epoch: [2][755/792]\tTime 1.759 (1363.878)\tETA 0:01:05\tLoss 0.5354 (0.5393)\n",
      "Epoch: [2][760/792]\tTime 1.832 (1372.776)\tETA 0:00:58\tLoss 0.5412 (0.5393)\n",
      "Epoch: [2][765/792]\tTime 1.787 (1381.787)\tETA 0:00:48\tLoss 0.5383 (0.5393)\n",
      "Epoch: [2][770/792]\tTime 1.791 (1390.669)\tETA 0:00:39\tLoss 0.5415 (0.5393)\n",
      "Epoch: [2][775/792]\tTime 1.820 (1399.600)\tETA 0:00:30\tLoss 0.5377 (0.5393)\n",
      "Epoch: [2][780/792]\tTime 1.715 (1408.390)\tETA 0:00:20\tLoss 0.5381 (0.5393)\n",
      "Epoch: [2][785/792]\tTime 1.830 (1417.591)\tETA 0:00:12\tLoss 0.5407 (0.5393)\n",
      "Epoch: [2][790/792]\tTime 1.829 (1426.721)\tETA 0:00:03\tLoss 0.5480 (0.5393)\n",
      "Epoch: [3][0/792]\tTime 1.812 (1.812)\tETA 0:23:55\tLoss 0.5461 (0.5461)\n",
      "Epoch: [3][5/792]\tTime 1.771 (10.749)\tETA 0:23:13\tLoss 0.5369 (0.5412)\n",
      "Epoch: [3][10/792]\tTime 1.734 (19.553)\tETA 0:22:35\tLoss 0.5340 (0.5386)\n",
      "Epoch: [3][15/792]\tTime 1.859 (28.704)\tETA 0:24:04\tLoss 0.5427 (0.5382)\n",
      "Epoch: [3][20/792]\tTime 1.746 (37.680)\tETA 0:22:27\tLoss 0.5370 (0.5379)\n",
      "Epoch: [3][25/792]\tTime 1.755 (46.485)\tETA 0:22:26\tLoss 0.5380 (0.5379)\n",
      "Epoch: [3][30/792]\tTime 1.702 (55.319)\tETA 0:21:36\tLoss 0.5370 (0.5372)\n",
      "Epoch: [3][35/792]\tTime 1.734 (64.043)\tETA 0:21:52\tLoss 0.5381 (0.5371)\n",
      "Epoch: [3][40/792]\tTime 1.786 (72.854)\tETA 0:22:22\tLoss 0.5372 (0.5368)\n",
      "Epoch: [3][45/792]\tTime 1.741 (81.797)\tETA 0:21:40\tLoss 0.5356 (0.5368)\n",
      "Epoch: [3][50/792]\tTime 1.829 (90.781)\tETA 0:22:37\tLoss 0.5340 (0.5368)\n",
      "Epoch: [3][55/792]\tTime 1.790 (99.885)\tETA 0:21:59\tLoss 0.5306 (0.5366)\n",
      "Epoch: [3][60/792]\tTime 1.785 (108.911)\tETA 0:21:46\tLoss 0.5392 (0.5365)\n",
      "Epoch: [3][65/792]\tTime 1.734 (117.747)\tETA 0:21:00\tLoss 0.5367 (0.5366)\n",
      "Epoch: [3][70/792]\tTime 1.799 (126.659)\tETA 0:21:38\tLoss 0.5384 (0.5366)\n",
      "Epoch: [3][75/792]\tTime 1.783 (135.630)\tETA 0:21:18\tLoss 0.5338 (0.5366)\n",
      "Epoch: [3][80/792]\tTime 1.753 (144.753)\tETA 0:20:47\tLoss 0.5382 (0.5366)\n",
      "Epoch: [3][85/792]\tTime 1.753 (153.589)\tETA 0:20:39\tLoss 0.5356 (0.5365)\n",
      "Epoch: [3][90/792]\tTime 1.801 (162.514)\tETA 0:21:04\tLoss 0.5457 (0.5366)\n",
      "Epoch: [3][95/792]\tTime 2.092 (171.822)\tETA 0:24:18\tLoss 0.5366 (0.5366)\n",
      "Epoch: [3][100/792]\tTime 1.753 (180.901)\tETA 0:20:13\tLoss 0.5357 (0.5366)\n",
      "Epoch: [3][105/792]\tTime 1.800 (189.851)\tETA 0:20:36\tLoss 0.5374 (0.5365)\n",
      "Epoch: [3][110/792]\tTime 1.816 (198.964)\tETA 0:20:38\tLoss 0.5369 (0.5364)\n",
      "Epoch: [3][115/792]\tTime 1.808 (208.044)\tETA 0:20:23\tLoss 0.5353 (0.5363)\n",
      "Epoch: [3][120/792]\tTime 1.757 (216.931)\tETA 0:19:40\tLoss 0.5338 (0.5363)\n",
      "Epoch: [3][125/792]\tTime 1.749 (225.684)\tETA 0:19:26\tLoss 0.5368 (0.5364)\n",
      "Epoch: [3][130/792]\tTime 1.846 (234.645)\tETA 0:20:21\tLoss 0.5324 (0.5363)\n",
      "Epoch: [3][135/792]\tTime 1.867 (243.724)\tETA 0:20:26\tLoss 0.5322 (0.5363)\n",
      "Epoch: [3][140/792]\tTime 1.771 (252.582)\tETA 0:19:14\tLoss 0.5319 (0.5363)\n",
      "Epoch: [3][145/792]\tTime 1.737 (261.452)\tETA 0:18:43\tLoss 0.5350 (0.5363)\n",
      "Epoch: [3][150/792]\tTime 1.757 (270.556)\tETA 0:18:48\tLoss 0.5369 (0.5364)\n",
      "Epoch: [3][155/792]\tTime 1.778 (279.476)\tETA 0:18:52\tLoss 0.5351 (0.5363)\n",
      "Epoch: [3][160/792]\tTime 1.714 (288.173)\tETA 0:18:03\tLoss 0.5332 (0.5363)\n",
      "Epoch: [3][165/792]\tTime 1.786 (297.106)\tETA 0:18:39\tLoss 0.5335 (0.5363)\n",
      "Epoch: [3][170/792]\tTime 1.929 (306.165)\tETA 0:19:59\tLoss 0.5326 (0.5362)\n",
      "Epoch: [3][175/792]\tTime 1.799 (315.065)\tETA 0:18:30\tLoss 0.5363 (0.5362)\n",
      "Epoch: [3][180/792]\tTime 1.846 (324.034)\tETA 0:18:49\tLoss 0.5362 (0.5362)\n",
      "Epoch: [3][185/792]\tTime 1.774 (333.073)\tETA 0:17:56\tLoss 0.5376 (0.5361)\n",
      "Epoch: [3][190/792]\tTime 1.729 (341.920)\tETA 0:17:20\tLoss 0.5339 (0.5361)\n",
      "Epoch: [3][195/792]\tTime 1.763 (350.765)\tETA 0:17:32\tLoss 0.5334 (0.5361)\n",
      "Epoch: [3][200/792]\tTime 1.779 (359.783)\tETA 0:17:33\tLoss 0.5345 (0.5361)\n",
      "Epoch: [3][205/792]\tTime 1.815 (368.735)\tETA 0:17:45\tLoss 0.5363 (0.5360)\n",
      "Epoch: [3][210/792]\tTime 1.780 (377.602)\tETA 0:17:15\tLoss 0.5395 (0.5360)\n",
      "Epoch: [3][215/792]\tTime 1.950 (387.007)\tETA 0:18:44\tLoss 0.5348 (0.5361)\n",
      "Epoch: [3][220/792]\tTime 1.840 (396.051)\tETA 0:17:32\tLoss 0.5464 (0.5361)\n",
      "Epoch: [3][225/792]\tTime 1.815 (404.850)\tETA 0:17:09\tLoss 0.5396 (0.5362)\n",
      "Epoch: [3][230/792]\tTime 1.765 (413.801)\tETA 0:16:31\tLoss 0.5396 (0.5363)\n",
      "Epoch: [3][235/792]\tTime 1.794 (422.829)\tETA 0:16:39\tLoss 0.5388 (0.5363)\n",
      "Epoch: [3][240/792]\tTime 1.794 (431.809)\tETA 0:16:30\tLoss 0.5357 (0.5362)\n",
      "Epoch: [3][245/792]\tTime 1.779 (440.987)\tETA 0:16:12\tLoss 0.5365 (0.5362)\n",
      "Epoch: [3][250/792]\tTime 1.769 (449.930)\tETA 0:15:58\tLoss 0.5320 (0.5362)\n",
      "Epoch: [3][255/792]\tTime 1.804 (458.818)\tETA 0:16:08\tLoss 0.5408 (0.5362)\n",
      "Epoch: [3][260/792]\tTime 1.749 (467.720)\tETA 0:15:30\tLoss 0.5346 (0.5362)\n",
      "Epoch: [3][265/792]\tTime 1.766 (476.463)\tETA 0:15:30\tLoss 0.5333 (0.5362)\n",
      "Epoch: [3][270/792]\tTime 2.319 (486.796)\tETA 0:20:10\tLoss 0.5403 (0.5363)\n",
      "Epoch: [3][275/792]\tTime 1.780 (495.986)\tETA 0:15:20\tLoss 0.5404 (0.5363)\n",
      "Epoch: [3][280/792]\tTime 2.322 (505.908)\tETA 0:19:48\tLoss 0.5398 (0.5365)\n",
      "Epoch: [3][285/792]\tTime 1.703 (515.259)\tETA 0:14:23\tLoss 0.5380 (0.5366)\n",
      "Epoch: [3][290/792]\tTime 1.801 (524.242)\tETA 0:15:04\tLoss 0.5369 (0.5366)\n",
      "Epoch: [3][295/792]\tTime 1.789 (533.038)\tETA 0:14:49\tLoss 0.5310 (0.5366)\n",
      "Epoch: [3][300/792]\tTime 1.804 (541.915)\tETA 0:14:47\tLoss 0.5368 (0.5365)\n",
      "Epoch: [3][305/792]\tTime 1.883 (551.036)\tETA 0:15:17\tLoss 0.5318 (0.5365)\n",
      "Epoch: [3][310/792]\tTime 1.798 (560.076)\tETA 0:14:26\tLoss 0.5344 (0.5365)\n",
      "Epoch: [3][315/792]\tTime 1.829 (569.206)\tETA 0:14:32\tLoss 0.5327 (0.5364)\n",
      "Epoch: [3][320/792]\tTime 1.785 (578.601)\tETA 0:14:02\tLoss 0.5328 (0.5364)\n",
      "Epoch: [3][325/792]\tTime 1.773 (587.514)\tETA 0:13:48\tLoss 0.5326 (0.5363)\n",
      "Epoch: [3][330/792]\tTime 1.873 (596.502)\tETA 0:14:25\tLoss 0.5354 (0.5363)\n",
      "Epoch: [3][335/792]\tTime 1.795 (605.612)\tETA 0:13:40\tLoss 0.5311 (0.5363)\n",
      "Epoch: [3][340/792]\tTime 1.777 (614.375)\tETA 0:13:23\tLoss 0.5333 (0.5362)\n",
      "Epoch: [3][345/792]\tTime 1.856 (623.486)\tETA 0:13:49\tLoss 0.5384 (0.5363)\n",
      "Epoch: [3][350/792]\tTime 1.857 (632.707)\tETA 0:13:40\tLoss 0.5340 (0.5362)\n",
      "Epoch: [3][355/792]\tTime 1.933 (642.072)\tETA 0:14:04\tLoss 0.5332 (0.5362)\n",
      "Epoch: [3][360/792]\tTime 1.715 (650.878)\tETA 0:12:20\tLoss 0.5360 (0.5362)\n",
      "Epoch: [3][365/792]\tTime 1.753 (659.686)\tETA 0:12:28\tLoss 0.5364 (0.5361)\n",
      "Epoch: [3][370/792]\tTime 1.783 (668.655)\tETA 0:12:32\tLoss 0.5346 (0.5361)\n",
      "Epoch: [3][375/792]\tTime 1.823 (677.618)\tETA 0:12:40\tLoss 0.5344 (0.5361)\n",
      "Epoch: [3][380/792]\tTime 1.837 (686.546)\tETA 0:12:36\tLoss 0.5340 (0.5361)\n",
      "Epoch: [3][385/792]\tTime 1.783 (695.401)\tETA 0:12:05\tLoss 0.5368 (0.5361)\n",
      "Epoch: [3][390/792]\tTime 1.924 (704.263)\tETA 0:12:53\tLoss 0.5416 (0.5361)\n",
      "Epoch: [3][395/792]\tTime 1.764 (713.153)\tETA 0:11:40\tLoss 0.5317 (0.5361)\n",
      "Epoch: [3][400/792]\tTime 1.766 (721.830)\tETA 0:11:32\tLoss 0.5361 (0.5361)\n",
      "Epoch: [3][405/792]\tTime 1.816 (730.925)\tETA 0:11:42\tLoss 0.5421 (0.5361)\n",
      "Epoch: [3][410/792]\tTime 1.805 (739.965)\tETA 0:11:29\tLoss 0.5439 (0.5362)\n",
      "Epoch: [3][415/792]\tTime 1.743 (748.896)\tETA 0:10:57\tLoss 0.5378 (0.5362)\n",
      "Epoch: [3][420/792]\tTime 1.769 (757.767)\tETA 0:10:57\tLoss 0.5364 (0.5362)\n",
      "Epoch: [3][425/792]\tTime 1.637 (766.482)\tETA 0:10:00\tLoss 0.5313 (0.5362)\n",
      "Epoch: [3][430/792]\tTime 1.843 (775.470)\tETA 0:11:07\tLoss 0.5323 (0.5362)\n",
      "Epoch: [3][435/792]\tTime 1.707 (785.156)\tETA 0:10:09\tLoss 0.5378 (0.5362)\n",
      "Epoch: [3][440/792]\tTime 1.772 (794.382)\tETA 0:10:23\tLoss 0.5326 (0.5362)\n",
      "Epoch: [3][445/792]\tTime 2.204 (803.843)\tETA 0:12:44\tLoss 0.5397 (0.5362)\n",
      "Epoch: [3][450/792]\tTime 1.793 (812.826)\tETA 0:10:13\tLoss 0.5326 (0.5362)\n",
      "Epoch: [3][455/792]\tTime 1.824 (821.738)\tETA 0:10:14\tLoss 0.5335 (0.5362)\n",
      "Epoch: [3][460/792]\tTime 1.746 (830.686)\tETA 0:09:39\tLoss 0.5320 (0.5362)\n",
      "Epoch: [3][465/792]\tTime 1.834 (839.750)\tETA 0:09:59\tLoss 0.5354 (0.5361)\n",
      "Epoch: [3][470/792]\tTime 1.851 (848.882)\tETA 0:09:56\tLoss 0.5376 (0.5361)\n",
      "Epoch: [3][475/792]\tTime 1.821 (857.917)\tETA 0:09:37\tLoss 0.5353 (0.5361)\n",
      "Epoch: [3][480/792]\tTime 1.774 (866.811)\tETA 0:09:13\tLoss 0.5351 (0.5361)\n",
      "Epoch: [3][485/792]\tTime 1.833 (875.858)\tETA 0:09:22\tLoss 0.5335 (0.5360)\n",
      "Epoch: [3][490/792]\tTime 1.728 (884.652)\tETA 0:08:41\tLoss 0.5306 (0.5360)\n",
      "Epoch: [3][495/792]\tTime 1.735 (893.473)\tETA 0:08:35\tLoss 0.5364 (0.5360)\n",
      "Epoch: [3][500/792]\tTime 1.833 (902.538)\tETA 0:08:55\tLoss 0.5369 (0.5360)\n",
      "Epoch: [3][505/792]\tTime 1.758 (911.578)\tETA 0:08:24\tLoss 0.5321 (0.5360)\n",
      "Epoch: [3][510/792]\tTime 1.839 (920.701)\tETA 0:08:38\tLoss 0.5362 (0.5360)\n",
      "Epoch: [3][515/792]\tTime 1.761 (929.878)\tETA 0:08:07\tLoss 0.5317 (0.5360)\n",
      "Epoch: [3][520/792]\tTime 1.818 (938.775)\tETA 0:08:14\tLoss 0.5336 (0.5360)\n",
      "Epoch: [3][525/792]\tTime 1.745 (947.616)\tETA 0:07:45\tLoss 0.5386 (0.5360)\n",
      "Epoch: [3][530/792]\tTime 1.707 (956.533)\tETA 0:07:27\tLoss 0.5327 (0.5359)\n",
      "Epoch: [3][535/792]\tTime 1.796 (965.648)\tETA 0:07:41\tLoss 0.5339 (0.5359)\n",
      "Epoch: [3][540/792]\tTime 1.739 (974.902)\tETA 0:07:18\tLoss 0.5350 (0.5359)\n",
      "Epoch: [3][545/792]\tTime 1.775 (983.875)\tETA 0:07:18\tLoss 0.5297 (0.5359)\n",
      "Epoch: [3][550/792]\tTime 1.813 (993.000)\tETA 0:07:18\tLoss 0.5347 (0.5358)\n",
      "Epoch: [3][555/792]\tTime 1.820 (1001.986)\tETA 0:07:11\tLoss 0.5332 (0.5358)\n",
      "Epoch: [3][560/792]\tTime 1.878 (1010.821)\tETA 0:07:15\tLoss 0.5345 (0.5358)\n",
      "Epoch: [3][565/792]\tTime 1.729 (1020.017)\tETA 0:06:32\tLoss 0.5319 (0.5357)\n",
      "Epoch: [3][570/792]\tTime 1.797 (1029.044)\tETA 0:06:38\tLoss 0.5307 (0.5357)\n",
      "Epoch: [3][575/792]\tTime 1.822 (1037.888)\tETA 0:06:35\tLoss 0.5337 (0.5357)\n",
      "Epoch: [3][580/792]\tTime 1.798 (1046.690)\tETA 0:06:21\tLoss 0.5334 (0.5357)\n",
      "Epoch: [3][585/792]\tTime 1.802 (1055.427)\tETA 0:06:12\tLoss 0.5344 (0.5357)\n",
      "Epoch: [3][590/792]\tTime 1.767 (1064.471)\tETA 0:05:56\tLoss 0.5311 (0.5357)\n",
      "Epoch: [3][595/792]\tTime 1.791 (1073.441)\tETA 0:05:52\tLoss 0.5330 (0.5356)\n",
      "Epoch: [3][600/792]\tTime 1.761 (1082.419)\tETA 0:05:38\tLoss 0.5324 (0.5356)\n",
      "Epoch: [3][605/792]\tTime 1.859 (1091.436)\tETA 0:05:47\tLoss 0.5373 (0.5357)\n",
      "Epoch: [3][610/792]\tTime 1.833 (1100.329)\tETA 0:05:33\tLoss 0.5330 (0.5357)\n",
      "Epoch: [3][615/792]\tTime 1.737 (1109.574)\tETA 0:05:07\tLoss 0.5360 (0.5357)\n",
      "Epoch: [3][620/792]\tTime 1.781 (1118.566)\tETA 0:05:06\tLoss 0.5319 (0.5357)\n",
      "Epoch: [3][625/792]\tTime 1.723 (1127.354)\tETA 0:04:47\tLoss 0.5358 (0.5356)\n",
      "Epoch: [3][630/792]\tTime 1.733 (1136.301)\tETA 0:04:40\tLoss 0.5332 (0.5357)\n",
      "Epoch: [3][635/792]\tTime 1.820 (1145.354)\tETA 0:04:45\tLoss 0.5315 (0.5356)\n",
      "Epoch: [3][640/792]\tTime 1.783 (1154.297)\tETA 0:04:30\tLoss 0.5348 (0.5357)\n",
      "Epoch: [3][645/792]\tTime 1.725 (1163.345)\tETA 0:04:13\tLoss 0.5363 (0.5357)\n",
      "Epoch: [3][650/792]\tTime 1.780 (1172.238)\tETA 0:04:12\tLoss 0.5368 (0.5357)\n",
      "Epoch: [3][655/792]\tTime 1.801 (1181.314)\tETA 0:04:06\tLoss 0.5363 (0.5356)\n",
      "Epoch: [3][660/792]\tTime 1.785 (1190.178)\tETA 0:03:55\tLoss 0.5323 (0.5356)\n",
      "Epoch: [3][665/792]\tTime 1.729 (1198.938)\tETA 0:03:39\tLoss 0.5354 (0.5356)\n",
      "Epoch: [3][670/792]\tTime 1.771 (1208.331)\tETA 0:03:36\tLoss 0.5319 (0.5356)\n",
      "Epoch: [3][675/792]\tTime 1.815 (1217.412)\tETA 0:03:32\tLoss 0.5319 (0.5356)\n",
      "Epoch: [3][680/792]\tTime 1.758 (1226.360)\tETA 0:03:16\tLoss 0.5327 (0.5356)\n",
      "Epoch: [3][685/792]\tTime 1.867 (1235.259)\tETA 0:03:19\tLoss 0.5324 (0.5356)\n",
      "Epoch: [3][690/792]\tTime 1.811 (1244.167)\tETA 0:03:04\tLoss 0.5360 (0.5356)\n",
      "Epoch: [3][695/792]\tTime 1.760 (1252.965)\tETA 0:02:50\tLoss 0.5301 (0.5356)\n",
      "Epoch: [3][700/792]\tTime 1.748 (1261.922)\tETA 0:02:40\tLoss 0.5363 (0.5356)\n",
      "Epoch: [3][705/792]\tTime 1.733 (1270.765)\tETA 0:02:30\tLoss 0.5344 (0.5356)\n",
      "Epoch: [3][710/792]\tTime 1.812 (1279.654)\tETA 0:02:28\tLoss 0.5364 (0.5356)\n",
      "Epoch: [3][715/792]\tTime 1.774 (1288.558)\tETA 0:02:16\tLoss 0.5358 (0.5356)\n",
      "Epoch: [3][720/792]\tTime 1.705 (1297.397)\tETA 0:02:02\tLoss 0.5392 (0.5355)\n",
      "Epoch: [3][725/792]\tTime 1.781 (1306.319)\tETA 0:01:59\tLoss 0.5372 (0.5355)\n",
      "Epoch: [3][730/792]\tTime 1.755 (1315.172)\tETA 0:01:48\tLoss 0.5328 (0.5355)\n",
      "Epoch: [3][735/792]\tTime 1.757 (1323.951)\tETA 0:01:40\tLoss 0.5407 (0.5355)\n",
      "Epoch: [3][740/792]\tTime 1.758 (1332.824)\tETA 0:01:31\tLoss 0.5320 (0.5355)\n",
      "Epoch: [3][745/792]\tTime 1.781 (1341.783)\tETA 0:01:23\tLoss 0.5382 (0.5355)\n",
      "Epoch: [3][750/792]\tTime 1.719 (1350.724)\tETA 0:01:12\tLoss 0.5303 (0.5355)\n",
      "Epoch: [3][755/792]\tTime 1.750 (1359.687)\tETA 0:01:04\tLoss 0.5326 (0.5355)\n",
      "Epoch: [3][760/792]\tTime 1.777 (1368.499)\tETA 0:00:56\tLoss 0.5292 (0.5355)\n",
      "Epoch: [3][765/792]\tTime 1.853 (1377.527)\tETA 0:00:50\tLoss 0.5349 (0.5355)\n",
      "Epoch: [3][770/792]\tTime 1.781 (1386.513)\tETA 0:00:39\tLoss 0.5360 (0.5355)\n",
      "Epoch: [3][775/792]\tTime 1.798 (1396.475)\tETA 0:00:30\tLoss 0.5332 (0.5355)\n",
      "Epoch: [3][780/792]\tTime 1.771 (1405.320)\tETA 0:00:21\tLoss 0.5323 (0.5354)\n",
      "Epoch: [3][785/792]\tTime 1.798 (1414.204)\tETA 0:00:12\tLoss 0.5334 (0.5354)\n",
      "Epoch: [3][790/792]\tTime 1.808 (1423.136)\tETA 0:00:03\tLoss 0.5362 (0.5354)\n",
      "Epoch: [4][0/792]\tTime 1.750 (1.750)\tETA 0:23:06\tLoss 0.5324 (0.5324)\n",
      "Epoch: [4][5/792]\tTime 1.762 (10.842)\tETA 0:23:06\tLoss 0.5326 (0.5330)\n",
      "Epoch: [4][10/792]\tTime 1.822 (19.880)\tETA 0:23:45\tLoss 0.5294 (0.5316)\n",
      "Epoch: [4][15/792]\tTime 1.745 (28.872)\tETA 0:22:35\tLoss 0.5304 (0.5319)\n",
      "Epoch: [4][20/792]\tTime 1.857 (38.162)\tETA 0:23:53\tLoss 0.5417 (0.5336)\n",
      "Epoch: [4][25/792]\tTime 1.725 (47.029)\tETA 0:22:03\tLoss 0.5342 (0.5345)\n",
      "Epoch: [4][30/792]\tTime 1.752 (55.905)\tETA 0:22:15\tLoss 0.5381 (0.5351)\n",
      "Epoch: [4][35/792]\tTime 1.820 (64.743)\tETA 0:22:57\tLoss 0.5322 (0.5348)\n",
      "Epoch: [4][40/792]\tTime 1.790 (73.648)\tETA 0:22:26\tLoss 0.5327 (0.5346)\n",
      "Epoch: [4][45/792]\tTime 1.749 (82.454)\tETA 0:21:46\tLoss 0.5335 (0.5345)\n",
      "Epoch: [4][50/792]\tTime 1.705 (91.240)\tETA 0:21:05\tLoss 0.5277 (0.5343)\n",
      "Epoch: [4][55/792]\tTime 1.782 (100.155)\tETA 0:21:53\tLoss 0.5305 (0.5344)\n",
      "Epoch: [4][60/792]\tTime 1.860 (109.154)\tETA 0:22:41\tLoss 0.5324 (0.5341)\n",
      "Epoch: [4][65/792]\tTime 1.733 (118.062)\tETA 0:20:59\tLoss 0.5359 (0.5341)\n",
      "Epoch: [4][70/792]\tTime 1.817 (126.917)\tETA 0:21:51\tLoss 0.5308 (0.5340)\n",
      "Epoch: [4][75/792]\tTime 1.835 (135.906)\tETA 0:21:55\tLoss 0.5366 (0.5340)\n",
      "Epoch: [4][80/792]\tTime 1.808 (144.874)\tETA 0:21:27\tLoss 0.5406 (0.5343)\n",
      "Epoch: [4][85/792]\tTime 1.804 (153.823)\tETA 0:21:15\tLoss 0.5314 (0.5343)\n",
      "Epoch: [4][90/792]\tTime 1.792 (162.880)\tETA 0:20:58\tLoss 0.5362 (0.5344)\n",
      "Epoch: [4][95/792]\tTime 1.748 (172.691)\tETA 0:20:18\tLoss 0.5349 (0.5344)\n",
      "Epoch: [4][100/792]\tTime 1.744 (181.648)\tETA 0:20:07\tLoss 0.5342 (0.5343)\n",
      "Epoch: [4][105/792]\tTime 1.835 (190.744)\tETA 0:21:00\tLoss 0.5296 (0.5341)\n",
      "Epoch: [4][110/792]\tTime 1.760 (199.593)\tETA 0:20:00\tLoss 0.5328 (0.5340)\n",
      "Epoch: [4][115/792]\tTime 1.850 (208.557)\tETA 0:20:52\tLoss 0.5310 (0.5339)\n",
      "Epoch: [4][120/792]\tTime 1.756 (217.565)\tETA 0:19:39\tLoss 0.5349 (0.5338)\n",
      "Epoch: [4][125/792]\tTime 1.824 (226.479)\tETA 0:20:16\tLoss 0.5293 (0.5337)\n",
      "Epoch: [4][130/792]\tTime 1.823 (236.022)\tETA 0:20:07\tLoss 0.5320 (0.5337)\n",
      "Epoch: [4][135/792]\tTime 1.790 (244.973)\tETA 0:19:36\tLoss 0.5349 (0.5336)\n",
      "Epoch: [4][140/792]\tTime 1.781 (254.091)\tETA 0:19:21\tLoss 0.5347 (0.5335)\n",
      "Epoch: [4][145/792]\tTime 1.751 (262.899)\tETA 0:18:52\tLoss 0.5328 (0.5335)\n",
      "Epoch: [4][150/792]\tTime 1.776 (271.940)\tETA 0:19:00\tLoss 0.5368 (0.5336)\n",
      "Epoch: [4][155/792]\tTime 1.784 (281.177)\tETA 0:18:56\tLoss 0.5313 (0.5336)\n",
      "Epoch: [4][160/792]\tTime 1.842 (290.059)\tETA 0:19:23\tLoss 0.5334 (0.5336)\n",
      "Epoch: [4][165/792]\tTime 2.133 (299.645)\tETA 0:22:17\tLoss 0.5374 (0.5336)\n",
      "Epoch: [4][170/792]\tTime 1.858 (308.743)\tETA 0:19:15\tLoss 0.5311 (0.5336)\n",
      "Epoch: [4][175/792]\tTime 1.769 (317.808)\tETA 0:18:11\tLoss 0.5330 (0.5335)\n",
      "Epoch: [4][180/792]\tTime 1.822 (326.885)\tETA 0:18:35\tLoss 0.5332 (0.5335)\n",
      "Epoch: [4][185/792]\tTime 1.764 (335.664)\tETA 0:17:50\tLoss 0.5304 (0.5334)\n",
      "Epoch: [4][190/792]\tTime 1.800 (344.661)\tETA 0:18:03\tLoss 0.5312 (0.5334)\n",
      "Epoch: [4][195/792]\tTime 1.777 (353.657)\tETA 0:17:40\tLoss 0.5326 (0.5334)\n",
      "Epoch: [4][200/792]\tTime 1.789 (363.261)\tETA 0:17:39\tLoss 0.5340 (0.5334)\n",
      "Epoch: [4][205/792]\tTime 1.847 (372.177)\tETA 0:18:03\tLoss 0.5341 (0.5334)\n",
      "Epoch: [4][210/792]\tTime 1.798 (381.082)\tETA 0:17:26\tLoss 0.5295 (0.5334)\n",
      "Epoch: [4][215/792]\tTime 1.791 (390.193)\tETA 0:17:13\tLoss 0.5349 (0.5334)\n",
      "Epoch: [4][220/792]\tTime 1.731 (399.059)\tETA 0:16:30\tLoss 0.5302 (0.5333)\n",
      "Epoch: [4][225/792]\tTime 1.777 (408.013)\tETA 0:16:47\tLoss 0.5297 (0.5333)\n",
      "Epoch: [4][230/792]\tTime 1.764 (417.085)\tETA 0:16:31\tLoss 0.5365 (0.5333)\n",
      "Epoch: [4][235/792]\tTime 1.775 (426.904)\tETA 0:16:28\tLoss 0.5275 (0.5332)\n",
      "Epoch: [4][240/792]\tTime 1.769 (435.923)\tETA 0:16:16\tLoss 0.5310 (0.5332)\n",
      "Epoch: [4][245/792]\tTime 1.886 (445.301)\tETA 0:17:11\tLoss 0.5325 (0.5332)\n",
      "Epoch: [4][250/792]\tTime 1.843 (454.305)\tETA 0:16:39\tLoss 0.5461 (0.5333)\n",
      "Epoch: [4][255/792]\tTime 1.818 (463.318)\tETA 0:16:16\tLoss 0.5301 (0.5333)\n",
      "Epoch: [4][260/792]\tTime 1.773 (472.284)\tETA 0:15:43\tLoss 0.5344 (0.5333)\n",
      "Epoch: [4][265/792]\tTime 1.768 (481.398)\tETA 0:15:31\tLoss 0.5391 (0.5333)\n",
      "Epoch: [4][270/792]\tTime 1.783 (490.239)\tETA 0:15:30\tLoss 0.5324 (0.5333)\n",
      "Epoch: [4][275/792]\tTime 1.780 (499.223)\tETA 0:15:20\tLoss 0.5384 (0.5334)\n",
      "Epoch: [4][280/792]\tTime 1.858 (508.141)\tETA 0:15:51\tLoss 0.5324 (0.5334)\n",
      "Epoch: [4][285/792]\tTime 1.937 (517.361)\tETA 0:16:22\tLoss 0.5330 (0.5334)\n",
      "Epoch: [4][290/792]\tTime 1.748 (526.296)\tETA 0:14:37\tLoss 0.5349 (0.5334)\n",
      "Epoch: [4][295/792]\tTime 1.711 (535.167)\tETA 0:14:10\tLoss 0.5353 (0.5335)\n",
      "Epoch: [4][300/792]\tTime 1.818 (544.008)\tETA 0:14:54\tLoss 0.5393 (0.5335)\n",
      "Epoch: [4][305/792]\tTime 1.794 (552.952)\tETA 0:14:33\tLoss 0.5332 (0.5335)\n",
      "Epoch: [4][310/792]\tTime 1.811 (561.880)\tETA 0:14:32\tLoss 0.5301 (0.5335)\n",
      "Epoch: [4][315/792]\tTime 1.764 (570.717)\tETA 0:14:01\tLoss 0.5334 (0.5335)\n",
      "Epoch: [4][320/792]\tTime 1.787 (579.567)\tETA 0:14:03\tLoss 0.5289 (0.5335)\n",
      "Epoch: [4][325/792]\tTime 1.783 (588.534)\tETA 0:13:52\tLoss 0.5301 (0.5334)\n",
      "Epoch: [4][330/792]\tTime 1.790 (597.439)\tETA 0:13:46\tLoss 0.5326 (0.5334)\n",
      "Epoch: [4][335/792]\tTime 1.819 (606.740)\tETA 0:13:51\tLoss 0.5294 (0.5334)\n",
      "Epoch: [4][340/792]\tTime 1.810 (615.814)\tETA 0:13:37\tLoss 0.5310 (0.5334)\n",
      "Epoch: [4][345/792]\tTime 1.787 (624.813)\tETA 0:13:18\tLoss 0.5316 (0.5333)\n",
      "Epoch: [4][350/792]\tTime 1.738 (633.654)\tETA 0:12:48\tLoss 0.5308 (0.5333)\n",
      "Epoch: [4][355/792]\tTime 1.797 (642.400)\tETA 0:13:05\tLoss 0.5361 (0.5333)\n",
      "Epoch: [4][360/792]\tTime 1.771 (651.295)\tETA 0:12:45\tLoss 0.5309 (0.5333)\n",
      "Epoch: [4][365/792]\tTime 1.746 (660.215)\tETA 0:12:25\tLoss 0.5301 (0.5333)\n",
      "Epoch: [4][370/792]\tTime 1.788 (669.139)\tETA 0:12:34\tLoss 0.5326 (0.5333)\n",
      "Epoch: [4][375/792]\tTime 1.743 (678.058)\tETA 0:12:06\tLoss 0.5290 (0.5333)\n",
      "Epoch: [4][380/792]\tTime 1.717 (686.865)\tETA 0:11:47\tLoss 0.5327 (0.5333)\n",
      "Epoch: [4][385/792]\tTime 1.751 (695.653)\tETA 0:11:52\tLoss 0.5308 (0.5332)\n",
      "Epoch: [4][390/792]\tTime 1.814 (704.555)\tETA 0:12:09\tLoss 0.5333 (0.5332)\n",
      "Epoch: [4][395/792]\tTime 1.718 (713.392)\tETA 0:11:22\tLoss 0.5360 (0.5332)\n",
      "Epoch: [4][400/792]\tTime 1.772 (722.341)\tETA 0:11:34\tLoss 0.5320 (0.5333)\n",
      "Epoch: [4][405/792]\tTime 1.863 (731.247)\tETA 0:12:01\tLoss 0.5302 (0.5332)\n",
      "Epoch: [4][410/792]\tTime 1.774 (740.374)\tETA 0:11:17\tLoss 0.5361 (0.5332)\n",
      "Epoch: [4][415/792]\tTime 1.772 (749.415)\tETA 0:11:08\tLoss 0.5326 (0.5332)\n",
      "Epoch: [4][420/792]\tTime 1.774 (758.442)\tETA 0:11:00\tLoss 0.5366 (0.5332)\n",
      "Epoch: [4][425/792]\tTime 1.756 (767.779)\tETA 0:10:44\tLoss 0.5338 (0.5332)\n",
      "Epoch: [4][430/792]\tTime 1.930 (776.862)\tETA 0:11:38\tLoss 0.5436 (0.5333)\n",
      "Epoch: [4][435/792]\tTime 1.826 (786.183)\tETA 0:10:52\tLoss 0.5299 (0.5333)\n",
      "Epoch: [4][440/792]\tTime 1.768 (795.090)\tETA 0:10:22\tLoss 0.5326 (0.5333)\n",
      "Epoch: [4][445/792]\tTime 1.836 (804.437)\tETA 0:10:37\tLoss 0.5325 (0.5332)\n",
      "Epoch: [4][450/792]\tTime 1.750 (813.388)\tETA 0:09:58\tLoss 0.5375 (0.5333)\n",
      "Epoch: [4][455/792]\tTime 1.719 (822.402)\tETA 0:09:39\tLoss 0.5363 (0.5333)\n",
      "Epoch: [4][460/792]\tTime 1.775 (831.393)\tETA 0:09:49\tLoss 0.5292 (0.5332)\n",
      "Epoch: [4][465/792]\tTime 1.771 (840.343)\tETA 0:09:39\tLoss 0.5352 (0.5332)\n",
      "Epoch: [4][470/792]\tTime 1.792 (849.178)\tETA 0:09:36\tLoss 0.5343 (0.5332)\n",
      "Epoch: [4][475/792]\tTime 1.797 (858.138)\tETA 0:09:29\tLoss 0.5345 (0.5332)\n",
      "Epoch: [4][480/792]\tTime 1.907 (867.369)\tETA 0:09:54\tLoss 0.5307 (0.5332)\n",
      "Epoch: [4][485/792]\tTime 1.732 (876.154)\tETA 0:08:51\tLoss 0.5351 (0.5333)\n",
      "Epoch: [4][490/792]\tTime 1.834 (885.513)\tETA 0:09:13\tLoss 0.5350 (0.5333)\n",
      "Epoch: [4][495/792]\tTime 1.797 (894.448)\tETA 0:08:53\tLoss 0.5337 (0.5333)\n",
      "Epoch: [4][500/792]\tTime 1.685 (903.240)\tETA 0:08:11\tLoss 0.5322 (0.5333)\n",
      "Epoch: [4][505/792]\tTime 1.929 (912.627)\tETA 0:09:13\tLoss 0.5335 (0.5333)\n",
      "Epoch: [4][510/792]\tTime 1.826 (921.904)\tETA 0:08:34\tLoss 0.5326 (0.5333)\n",
      "Epoch: [4][515/792]\tTime 1.870 (931.226)\tETA 0:08:37\tLoss 0.5390 (0.5334)\n",
      "Epoch: [4][520/792]\tTime 1.725 (940.384)\tETA 0:07:49\tLoss 0.5336 (0.5334)\n",
      "Epoch: [4][525/792]\tTime 1.749 (949.235)\tETA 0:07:46\tLoss 0.5291 (0.5333)\n",
      "Epoch: [4][530/792]\tTime 1.804 (958.249)\tETA 0:07:52\tLoss 0.5308 (0.5333)\n",
      "Epoch: [4][535/792]\tTime 1.740 (967.225)\tETA 0:07:27\tLoss 0.5374 (0.5333)\n",
      "Epoch: [4][540/792]\tTime 1.795 (976.207)\tETA 0:07:32\tLoss 0.5327 (0.5333)\n",
      "Epoch: [4][545/792]\tTime 1.836 (985.286)\tETA 0:07:33\tLoss 0.5390 (0.5334)\n",
      "Epoch: [4][550/792]\tTime 1.843 (994.300)\tETA 0:07:26\tLoss 0.5305 (0.5333)\n",
      "Epoch: [4][555/792]\tTime 1.852 (1003.265)\tETA 0:07:18\tLoss 0.5321 (0.5333)\n",
      "Epoch: [4][560/792]\tTime 1.788 (1011.968)\tETA 0:06:54\tLoss 0.5323 (0.5333)\n",
      "Epoch: [4][565/792]\tTime 1.804 (1020.957)\tETA 0:06:49\tLoss 0.5308 (0.5333)\n",
      "Epoch: [4][570/792]\tTime 2.067 (1030.069)\tETA 0:07:38\tLoss 0.5305 (0.5333)\n",
      "Epoch: [4][575/792]\tTime 1.845 (1039.061)\tETA 0:06:40\tLoss 0.5323 (0.5333)\n",
      "Epoch: [4][580/792]\tTime 1.925 (1048.097)\tETA 0:06:48\tLoss 0.5353 (0.5333)\n",
      "Epoch: [4][585/792]\tTime 1.815 (1057.172)\tETA 0:06:15\tLoss 0.5330 (0.5333)\n",
      "Epoch: [4][590/792]\tTime 1.729 (1066.197)\tETA 0:05:49\tLoss 0.5300 (0.5332)\n",
      "Epoch: [4][595/792]\tTime 1.741 (1075.639)\tETA 0:05:42\tLoss 0.5288 (0.5332)\n",
      "Epoch: [4][600/792]\tTime 1.778 (1084.799)\tETA 0:05:41\tLoss 0.5320 (0.5332)\n",
      "Epoch: [4][605/792]\tTime 1.776 (1093.723)\tETA 0:05:32\tLoss 0.5341 (0.5332)\n",
      "Epoch: [4][610/792]\tTime 1.747 (1102.844)\tETA 0:05:17\tLoss 0.5343 (0.5332)\n",
      "Epoch: [4][615/792]\tTime 1.774 (1111.874)\tETA 0:05:14\tLoss 0.5309 (0.5332)\n",
      "Epoch: [4][620/792]\tTime 1.786 (1120.805)\tETA 0:05:07\tLoss 0.5285 (0.5332)\n",
      "Epoch: [4][625/792]\tTime 1.808 (1130.129)\tETA 0:05:01\tLoss 0.5275 (0.5331)\n",
      "Epoch: [4][630/792]\tTime 1.760 (1139.010)\tETA 0:04:45\tLoss 0.5329 (0.5331)\n",
      "Epoch: [4][635/792]\tTime 1.764 (1147.825)\tETA 0:04:37\tLoss 0.5322 (0.5331)\n",
      "Epoch: [4][640/792]\tTime 1.815 (1157.046)\tETA 0:04:35\tLoss 0.5343 (0.5331)\n",
      "Epoch: [4][645/792]\tTime 1.807 (1166.122)\tETA 0:04:25\tLoss 0.5322 (0.5331)\n",
      "Epoch: [4][650/792]\tTime 1.948 (1175.141)\tETA 0:04:36\tLoss 0.5319 (0.5332)\n",
      "Epoch: [4][655/792]\tTime 1.785 (1184.572)\tETA 0:04:04\tLoss 0.5389 (0.5332)\n",
      "Epoch: [4][660/792]\tTime 1.714 (1193.529)\tETA 0:03:46\tLoss 0.5319 (0.5332)\n",
      "Epoch: [4][665/792]\tTime 1.776 (1202.380)\tETA 0:03:45\tLoss 0.5314 (0.5332)\n",
      "Epoch: [4][670/792]\tTime 1.782 (1211.476)\tETA 0:03:37\tLoss 0.5320 (0.5332)\n",
      "Epoch: [4][675/792]\tTime 1.770 (1220.341)\tETA 0:03:27\tLoss 0.5329 (0.5332)\n",
      "Epoch: [4][680/792]\tTime 1.837 (1229.349)\tETA 0:03:25\tLoss 0.5317 (0.5331)\n",
      "Epoch: [4][685/792]\tTime 1.758 (1238.112)\tETA 0:03:08\tLoss 0.5334 (0.5331)\n",
      "Epoch: [4][690/792]\tTime 2.287 (1248.109)\tETA 0:03:53\tLoss 0.5306 (0.5331)\n",
      "Epoch: [4][695/792]\tTime 1.752 (1257.248)\tETA 0:02:49\tLoss 0.5325 (0.5331)\n",
      "Epoch: [4][700/792]\tTime 1.955 (1266.284)\tETA 0:02:59\tLoss 0.5308 (0.5331)\n",
      "Epoch: [4][705/792]\tTime 1.762 (1275.286)\tETA 0:02:33\tLoss 0.5326 (0.5331)\n",
      "Epoch: [4][710/792]\tTime 1.703 (1284.318)\tETA 0:02:19\tLoss 0.5316 (0.5331)\n",
      "Epoch: [4][715/792]\tTime 1.777 (1293.256)\tETA 0:02:16\tLoss 0.5296 (0.5331)\n",
      "Epoch: [4][720/792]\tTime 1.812 (1302.139)\tETA 0:02:10\tLoss 0.5340 (0.5331)\n",
      "Epoch: [4][725/792]\tTime 1.775 (1311.209)\tETA 0:01:58\tLoss 0.5288 (0.5331)\n",
      "Epoch: [4][730/792]\tTime 1.855 (1320.230)\tETA 0:01:55\tLoss 0.5329 (0.5331)\n",
      "Epoch: [4][735/792]\tTime 1.781 (1329.217)\tETA 0:01:41\tLoss 0.5328 (0.5331)\n",
      "Epoch: [4][740/792]\tTime 1.797 (1338.174)\tETA 0:01:33\tLoss 0.5343 (0.5331)\n",
      "Epoch: [4][745/792]\tTime 1.871 (1347.266)\tETA 0:01:27\tLoss 0.5333 (0.5331)\n",
      "Epoch: [4][750/792]\tTime 1.795 (1356.223)\tETA 0:01:15\tLoss 0.5303 (0.5331)\n",
      "Epoch: [4][755/792]\tTime 1.770 (1365.141)\tETA 0:01:05\tLoss 0.5349 (0.5331)\n",
      "Epoch: [4][760/792]\tTime 1.778 (1373.997)\tETA 0:00:56\tLoss 0.5329 (0.5331)\n",
      "Epoch: [4][765/792]\tTime 1.802 (1382.843)\tETA 0:00:48\tLoss 0.5321 (0.5331)\n",
      "Epoch: [4][770/792]\tTime 1.790 (1391.610)\tETA 0:00:39\tLoss 0.5322 (0.5331)\n",
      "Epoch: [4][775/792]\tTime 1.791 (1400.302)\tETA 0:00:30\tLoss 0.5308 (0.5331)\n",
      "Epoch: [4][780/792]\tTime 1.752 (1409.157)\tETA 0:00:21\tLoss 0.5310 (0.5331)\n",
      "Epoch: [4][785/792]\tTime 1.762 (1417.993)\tETA 0:00:12\tLoss 0.5382 (0.5331)\n",
      "Epoch: [4][790/792]\tTime 1.795 (1426.956)\tETA 0:00:03\tLoss 0.5409 (0.5331)\n",
      "Epoch: [5][0/792]\tTime 1.762 (1.762)\tETA 0:23:15\tLoss 0.5329 (0.5329)\n",
      "Epoch: [5][5/792]\tTime 1.796 (10.990)\tETA 0:23:33\tLoss 0.5349 (0.5335)\n",
      "Epoch: [5][10/792]\tTime 1.805 (19.887)\tETA 0:23:31\tLoss 0.5295 (0.5318)\n",
      "Epoch: [5][15/792]\tTime 1.737 (28.959)\tETA 0:22:29\tLoss 0.5329 (0.5324)\n",
      "Epoch: [5][20/792]\tTime 1.760 (38.267)\tETA 0:22:38\tLoss 0.5318 (0.5322)\n",
      "Epoch: [5][25/792]\tTime 1.812 (47.469)\tETA 0:23:10\tLoss 0.5305 (0.5319)\n",
      "Epoch: [5][30/792]\tTime 1.824 (56.293)\tETA 0:23:10\tLoss 0.5319 (0.5318)\n",
      "Epoch: [5][35/792]\tTime 1.795 (65.178)\tETA 0:22:38\tLoss 0.5317 (0.5320)\n",
      "Epoch: [5][40/792]\tTime 1.752 (74.295)\tETA 0:21:57\tLoss 0.5310 (0.5319)\n",
      "Epoch: [5][45/792]\tTime 1.771 (83.264)\tETA 0:22:02\tLoss 0.5311 (0.5317)\n",
      "Epoch: [5][50/792]\tTime 1.750 (92.256)\tETA 0:21:38\tLoss 0.5331 (0.5318)\n",
      "Epoch: [5][55/792]\tTime 1.865 (101.367)\tETA 0:22:54\tLoss 0.5277 (0.5317)\n",
      "Epoch: [5][60/792]\tTime 1.771 (110.431)\tETA 0:21:36\tLoss 0.5341 (0.5320)\n",
      "Epoch: [5][65/792]\tTime 1.843 (119.254)\tETA 0:22:19\tLoss 0.5347 (0.5322)\n",
      "Epoch: [5][70/792]\tTime 1.782 (128.653)\tETA 0:21:26\tLoss 0.5294 (0.5321)\n",
      "Epoch: [5][75/792]\tTime 1.793 (137.714)\tETA 0:21:25\tLoss 0.5328 (0.5322)\n",
      "Epoch: [5][80/792]\tTime 1.758 (147.242)\tETA 0:20:51\tLoss 0.5274 (0.5321)\n",
      "Epoch: [5][85/792]\tTime 1.828 (156.269)\tETA 0:21:32\tLoss 0.5276 (0.5320)\n",
      "Epoch: [5][90/792]\tTime 1.818 (165.236)\tETA 0:21:16\tLoss 0.5289 (0.5319)\n",
      "Epoch: [5][95/792]\tTime 1.903 (174.387)\tETA 0:22:06\tLoss 0.5321 (0.5318)\n",
      "Epoch: [5][100/792]\tTime 1.792 (183.300)\tETA 0:20:39\tLoss 0.5299 (0.5317)\n",
      "Epoch: [5][105/792]\tTime 1.779 (192.457)\tETA 0:20:22\tLoss 0.5269 (0.5316)\n",
      "Epoch: [5][110/792]\tTime 1.737 (201.270)\tETA 0:19:44\tLoss 0.5299 (0.5315)\n",
      "Epoch: [5][115/792]\tTime 1.853 (210.463)\tETA 0:20:54\tLoss 0.5300 (0.5315)\n",
      "Epoch: [5][120/792]\tTime 1.833 (219.365)\tETA 0:20:31\tLoss 0.5297 (0.5314)\n",
      "Epoch: [5][125/792]\tTime 1.752 (228.194)\tETA 0:19:28\tLoss 0.5366 (0.5314)\n",
      "Epoch: [5][130/792]\tTime 1.764 (236.871)\tETA 0:19:28\tLoss 0.5304 (0.5314)\n",
      "Epoch: [5][135/792]\tTime 1.745 (245.690)\tETA 0:19:06\tLoss 0.5281 (0.5314)\n",
      "Epoch: [5][140/792]\tTime 1.814 (254.602)\tETA 0:19:42\tLoss 0.5301 (0.5313)\n",
      "Epoch: [5][145/792]\tTime 1.737 (263.486)\tETA 0:18:43\tLoss 0.5285 (0.5313)\n",
      "Epoch: [5][150/792]\tTime 1.739 (272.290)\tETA 0:18:36\tLoss 0.5293 (0.5312)\n",
      "Epoch: [5][155/792]\tTime 1.773 (281.341)\tETA 0:18:49\tLoss 0.5335 (0.5312)\n",
      "Epoch: [5][160/792]\tTime 1.756 (290.159)\tETA 0:18:29\tLoss 0.5311 (0.5312)\n",
      "Epoch: [5][165/792]\tTime 1.832 (299.054)\tETA 0:19:08\tLoss 0.5287 (0.5312)\n",
      "Epoch: [5][170/792]\tTime 1.823 (308.235)\tETA 0:18:53\tLoss 0.5306 (0.5312)\n",
      "Epoch: [5][175/792]\tTime 1.749 (317.112)\tETA 0:17:58\tLoss 0.5298 (0.5311)\n",
      "Epoch: [5][180/792]\tTime 1.806 (326.024)\tETA 0:18:25\tLoss 0.5351 (0.5311)\n",
      "Epoch: [5][185/792]\tTime 1.760 (335.059)\tETA 0:17:48\tLoss 0.5324 (0.5311)\n",
      "Epoch: [5][190/792]\tTime 1.824 (344.378)\tETA 0:18:18\tLoss 0.5318 (0.5311)\n",
      "Epoch: [5][195/792]\tTime 1.775 (353.501)\tETA 0:17:39\tLoss 0.5343 (0.5311)\n",
      "Epoch: [5][200/792]\tTime 1.804 (362.537)\tETA 0:17:48\tLoss 0.5267 (0.5311)\n",
      "Epoch: [5][205/792]\tTime 1.851 (371.627)\tETA 0:18:06\tLoss 0.5275 (0.5311)\n",
      "Epoch: [5][210/792]\tTime 1.819 (380.563)\tETA 0:17:38\tLoss 0.5340 (0.5312)\n",
      "Epoch: [5][215/792]\tTime 1.745 (389.576)\tETA 0:16:46\tLoss 0.5359 (0.5313)\n",
      "Epoch: [5][220/792]\tTime 1.835 (398.619)\tETA 0:17:29\tLoss 0.5333 (0.5314)\n",
      "Epoch: [5][225/792]\tTime 1.771 (407.743)\tETA 0:16:43\tLoss 0.5339 (0.5315)\n",
      "Epoch: [5][230/792]\tTime 1.867 (416.852)\tETA 0:17:29\tLoss 0.5432 (0.5316)\n",
      "Epoch: [5][235/792]\tTime 1.815 (426.021)\tETA 0:16:50\tLoss 0.5351 (0.5317)\n",
      "Epoch: [5][240/792]\tTime 2.003 (435.253)\tETA 0:18:25\tLoss 0.5280 (0.5317)\n",
      "Epoch: [5][245/792]\tTime 1.755 (444.361)\tETA 0:16:00\tLoss 0.5316 (0.5317)\n",
      "Epoch: [5][250/792]\tTime 1.793 (453.958)\tETA 0:16:11\tLoss 0.5348 (0.5318)\n",
      "Epoch: [5][255/792]\tTime 1.821 (463.248)\tETA 0:16:18\tLoss 0.5427 (0.5319)\n",
      "Epoch: [5][260/792]\tTime 1.777 (472.248)\tETA 0:15:45\tLoss 0.5400 (0.5320)\n",
      "Epoch: [5][265/792]\tTime 1.734 (481.460)\tETA 0:15:13\tLoss 0.5328 (0.5321)\n",
      "Epoch: [5][270/792]\tTime 1.889 (490.462)\tETA 0:16:26\tLoss 0.5301 (0.5321)\n",
      "Epoch: [5][275/792]\tTime 1.776 (499.303)\tETA 0:15:18\tLoss 0.5342 (0.5321)\n",
      "Epoch: [5][280/792]\tTime 1.757 (508.159)\tETA 0:14:59\tLoss 0.5338 (0.5322)\n",
      "Epoch: [5][285/792]\tTime 1.720 (516.921)\tETA 0:14:31\tLoss 0.5360 (0.5322)\n",
      "Epoch: [5][290/792]\tTime 1.833 (526.252)\tETA 0:15:20\tLoss 0.5347 (0.5323)\n",
      "Epoch: [5][295/792]\tTime 1.841 (535.314)\tETA 0:15:15\tLoss 0.5331 (0.5323)\n",
      "Epoch: [5][300/792]\tTime 1.788 (544.349)\tETA 0:14:39\tLoss 0.5318 (0.5323)\n",
      "Epoch: [5][305/792]\tTime 1.747 (553.246)\tETA 0:14:10\tLoss 0.5345 (0.5324)\n",
      "Epoch: [5][310/792]\tTime 1.806 (562.205)\tETA 0:14:30\tLoss 0.5289 (0.5324)\n",
      "Epoch: [5][315/792]\tTime 1.726 (571.232)\tETA 0:13:43\tLoss 0.5345 (0.5325)\n",
      "Epoch: [5][320/792]\tTime 1.845 (580.205)\tETA 0:14:30\tLoss 0.5320 (0.5324)\n",
      "Epoch: [5][325/792]\tTime 1.810 (589.412)\tETA 0:14:05\tLoss 0.5320 (0.5324)\n",
      "Epoch: [5][330/792]\tTime 1.737 (598.246)\tETA 0:13:22\tLoss 0.5323 (0.5324)\n",
      "Epoch: [5][335/792]\tTime 1.757 (607.107)\tETA 0:13:22\tLoss 0.5321 (0.5324)\n",
      "Epoch: [5][340/792]\tTime 1.829 (616.156)\tETA 0:13:46\tLoss 0.5307 (0.5324)\n",
      "Epoch: [5][345/792]\tTime 1.771 (625.042)\tETA 0:13:11\tLoss 0.5298 (0.5324)\n",
      "Epoch: [5][350/792]\tTime 1.774 (633.845)\tETA 0:13:04\tLoss 0.5317 (0.5324)\n",
      "Epoch: [5][355/792]\tTime 1.811 (642.689)\tETA 0:13:11\tLoss 0.5291 (0.5324)\n",
      "Epoch: [5][360/792]\tTime 1.824 (651.714)\tETA 0:13:08\tLoss 0.5281 (0.5323)\n",
      "Epoch: [5][365/792]\tTime 1.809 (660.620)\tETA 0:12:52\tLoss 0.5324 (0.5323)\n",
      "Epoch: [5][370/792]\tTime 1.752 (669.587)\tETA 0:12:19\tLoss 0.5300 (0.5323)\n",
      "Epoch: [5][375/792]\tTime 1.756 (678.353)\tETA 0:12:12\tLoss 0.5303 (0.5323)\n",
      "Epoch: [5][380/792]\tTime 1.768 (687.191)\tETA 0:12:08\tLoss 0.5310 (0.5322)\n",
      "Epoch: [5][385/792]\tTime 1.806 (696.215)\tETA 0:12:14\tLoss 0.5307 (0.5322)\n",
      "Epoch: [5][390/792]\tTime 1.778 (705.028)\tETA 0:11:54\tLoss 0.5291 (0.5322)\n",
      "Epoch: [5][395/792]\tTime 1.706 (713.927)\tETA 0:11:17\tLoss 0.5305 (0.5322)\n",
      "Epoch: [5][400/792]\tTime 1.777 (722.676)\tETA 0:11:36\tLoss 0.5290 (0.5321)\n",
      "Epoch: [5][405/792]\tTime 2.010 (732.057)\tETA 0:12:58\tLoss 0.5317 (0.5321)\n",
      "Epoch: [5][410/792]\tTime 1.779 (741.107)\tETA 0:11:19\tLoss 0.5356 (0.5321)\n",
      "Epoch: [5][415/792]\tTime 1.829 (749.940)\tETA 0:11:29\tLoss 0.5296 (0.5321)\n",
      "Epoch: [5][420/792]\tTime 1.787 (758.925)\tETA 0:11:04\tLoss 0.5292 (0.5321)\n",
      "Epoch: [5][425/792]\tTime 1.877 (768.197)\tETA 0:11:28\tLoss 0.5288 (0.5320)\n",
      "Epoch: [5][430/792]\tTime 1.858 (777.394)\tETA 0:11:12\tLoss 0.5301 (0.5320)\n",
      "Epoch: [5][435/792]\tTime 1.713 (786.145)\tETA 0:10:11\tLoss 0.5328 (0.5320)\n",
      "Epoch: [5][440/792]\tTime 1.725 (794.987)\tETA 0:10:07\tLoss 0.5297 (0.5320)\n",
      "Epoch: [5][445/792]\tTime 1.851 (803.850)\tETA 0:10:42\tLoss 0.5312 (0.5320)\n",
      "Epoch: [5][450/792]\tTime 1.733 (812.756)\tETA 0:09:52\tLoss 0.5309 (0.5320)\n",
      "Epoch: [5][455/792]\tTime 1.752 (821.582)\tETA 0:09:50\tLoss 0.5289 (0.5320)\n",
      "Epoch: [5][460/792]\tTime 1.711 (830.722)\tETA 0:09:28\tLoss 0.5339 (0.5320)\n",
      "Epoch: [5][465/792]\tTime 1.757 (839.501)\tETA 0:09:34\tLoss 0.5306 (0.5320)\n",
      "Epoch: [5][470/792]\tTime 1.770 (848.267)\tETA 0:09:30\tLoss 0.5277 (0.5320)\n",
      "Epoch: [5][475/792]\tTime 1.881 (857.244)\tETA 0:09:56\tLoss 0.5326 (0.5320)\n",
      "Epoch: [5][480/792]\tTime 1.764 (866.782)\tETA 0:09:10\tLoss 0.5296 (0.5320)\n",
      "Epoch: [5][485/792]\tTime 1.824 (876.142)\tETA 0:09:19\tLoss 0.5341 (0.5320)\n",
      "Epoch: [5][490/792]\tTime 1.783 (885.178)\tETA 0:08:58\tLoss 0.5294 (0.5320)\n",
      "Epoch: [5][495/792]\tTime 1.816 (894.241)\tETA 0:08:59\tLoss 0.5327 (0.5319)\n",
      "Epoch: [5][500/792]\tTime 1.800 (903.283)\tETA 0:08:45\tLoss 0.5346 (0.5319)\n",
      "Epoch: [5][505/792]\tTime 1.754 (912.117)\tETA 0:08:23\tLoss 0.5338 (0.5319)\n",
      "Epoch: [5][510/792]\tTime 1.789 (920.985)\tETA 0:08:24\tLoss 0.5346 (0.5319)\n",
      "Epoch: [5][515/792]\tTime 1.760 (929.974)\tETA 0:08:07\tLoss 0.5266 (0.5319)\n",
      "Epoch: [5][520/792]\tTime 1.784 (938.889)\tETA 0:08:05\tLoss 0.5292 (0.5319)\n",
      "Epoch: [5][525/792]\tTime 1.741 (947.714)\tETA 0:07:44\tLoss 0.5293 (0.5319)\n",
      "Epoch: [5][530/792]\tTime 1.848 (956.853)\tETA 0:08:04\tLoss 0.5296 (0.5318)\n",
      "Epoch: [5][535/792]\tTime 1.735 (966.012)\tETA 0:07:25\tLoss 0.5273 (0.5318)\n",
      "Epoch: [5][540/792]\tTime 1.829 (975.813)\tETA 0:07:40\tLoss 0.5282 (0.5318)\n",
      "Epoch: [5][545/792]\tTime 1.743 (984.631)\tETA 0:07:10\tLoss 0.5299 (0.5318)\n",
      "Epoch: [5][550/792]\tTime 1.757 (993.442)\tETA 0:07:05\tLoss 0.5324 (0.5318)\n",
      "Epoch: [5][555/792]\tTime 2.141 (1002.679)\tETA 0:08:27\tLoss 0.5330 (0.5318)\n",
      "Epoch: [5][560/792]\tTime 1.786 (1011.721)\tETA 0:06:54\tLoss 0.5309 (0.5318)\n",
      "Epoch: [5][565/792]\tTime 1.769 (1020.570)\tETA 0:06:41\tLoss 0.5320 (0.5318)\n",
      "Epoch: [5][570/792]\tTime 1.831 (1029.501)\tETA 0:06:46\tLoss 0.5280 (0.5317)\n",
      "Epoch: [5][575/792]\tTime 1.790 (1038.428)\tETA 0:06:28\tLoss 0.5313 (0.5317)\n",
      "Epoch: [5][580/792]\tTime 1.755 (1047.397)\tETA 0:06:11\tLoss 0.5309 (0.5317)\n",
      "Epoch: [5][585/792]\tTime 1.746 (1056.186)\tETA 0:06:01\tLoss 0.5335 (0.5317)\n",
      "Epoch: [5][590/792]\tTime 1.818 (1065.244)\tETA 0:06:07\tLoss 0.5305 (0.5317)\n",
      "Epoch: [5][595/792]\tTime 1.805 (1074.396)\tETA 0:05:55\tLoss 0.5319 (0.5317)\n",
      "Epoch: [5][600/792]\tTime 1.758 (1083.681)\tETA 0:05:37\tLoss 0.5276 (0.5317)\n",
      "Epoch: [5][605/792]\tTime 1.821 (1092.797)\tETA 0:05:40\tLoss 0.5285 (0.5317)\n",
      "Epoch: [5][610/792]\tTime 1.769 (1102.311)\tETA 0:05:21\tLoss 0.5348 (0.5317)\n",
      "Epoch: [5][615/792]\tTime 1.789 (1111.174)\tETA 0:05:16\tLoss 0.5302 (0.5317)\n",
      "Epoch: [5][620/792]\tTime 1.754 (1120.173)\tETA 0:05:01\tLoss 0.5305 (0.5317)\n",
      "Epoch: [5][625/792]\tTime 1.809 (1129.341)\tETA 0:05:02\tLoss 0.5310 (0.5317)\n",
      "Epoch: [5][630/792]\tTime 1.780 (1138.048)\tETA 0:04:48\tLoss 0.5309 (0.5316)\n",
      "Epoch: [5][635/792]\tTime 1.790 (1147.027)\tETA 0:04:41\tLoss 0.5282 (0.5316)\n",
      "Epoch: [5][640/792]\tTime 1.772 (1156.281)\tETA 0:04:29\tLoss 0.5299 (0.5316)\n",
      "Epoch: [5][645/792]\tTime 1.849 (1165.496)\tETA 0:04:31\tLoss 0.5323 (0.5316)\n",
      "Epoch: [5][650/792]\tTime 1.910 (1174.668)\tETA 0:04:31\tLoss 0.5268 (0.5316)\n",
      "Epoch: [5][655/792]\tTime 1.798 (1183.805)\tETA 0:04:06\tLoss 0.5318 (0.5316)\n",
      "Epoch: [5][660/792]\tTime 1.837 (1192.751)\tETA 0:04:02\tLoss 0.5327 (0.5316)\n",
      "Epoch: [5][665/792]\tTime 1.755 (1201.509)\tETA 0:03:42\tLoss 0.5350 (0.5316)\n",
      "Epoch: [5][670/792]\tTime 1.749 (1210.507)\tETA 0:03:33\tLoss 0.5344 (0.5316)\n",
      "Epoch: [5][675/792]\tTime 1.744 (1219.527)\tETA 0:03:24\tLoss 0.5328 (0.5316)\n",
      "Epoch: [5][680/792]\tTime 1.739 (1228.352)\tETA 0:03:14\tLoss 0.5366 (0.5317)\n",
      "Epoch: [5][685/792]\tTime 1.763 (1237.367)\tETA 0:03:08\tLoss 0.5403 (0.5317)\n",
      "Epoch: [5][690/792]\tTime 1.911 (1246.493)\tETA 0:03:14\tLoss 0.5325 (0.5317)\n",
      "Epoch: [5][695/792]\tTime 1.920 (1255.523)\tETA 0:03:06\tLoss 0.5279 (0.5317)\n",
      "Epoch: [5][700/792]\tTime 1.794 (1264.822)\tETA 0:02:45\tLoss 0.5343 (0.5317)\n",
      "Epoch: [5][705/792]\tTime 1.887 (1273.909)\tETA 0:02:44\tLoss 0.5340 (0.5317)\n",
      "Epoch: [5][710/792]\tTime 1.886 (1283.109)\tETA 0:02:34\tLoss 0.5301 (0.5317)\n",
      "Epoch: [5][715/792]\tTime 1.731 (1291.879)\tETA 0:02:13\tLoss 0.5342 (0.5317)\n",
      "Epoch: [5][720/792]\tTime 1.779 (1300.679)\tETA 0:02:08\tLoss 0.5334 (0.5317)\n",
      "Epoch: [5][725/792]\tTime 1.805 (1309.638)\tETA 0:02:00\tLoss 0.5373 (0.5317)\n",
      "Epoch: [5][730/792]\tTime 1.740 (1318.365)\tETA 0:01:47\tLoss 0.5324 (0.5317)\n",
      "Epoch: [5][735/792]\tTime 1.781 (1327.582)\tETA 0:01:41\tLoss 0.5397 (0.5317)\n",
      "Epoch: [5][740/792]\tTime 1.750 (1336.432)\tETA 0:01:31\tLoss 0.5325 (0.5317)\n",
      "Epoch: [5][745/792]\tTime 1.744 (1345.357)\tETA 0:01:21\tLoss 0.5299 (0.5317)\n",
      "Epoch: [5][750/792]\tTime 1.779 (1354.180)\tETA 0:01:14\tLoss 0.5300 (0.5317)\n",
      "Epoch: [5][755/792]\tTime 1.872 (1363.871)\tETA 0:01:09\tLoss 0.5350 (0.5317)\n",
      "Epoch: [5][760/792]\tTime 1.803 (1373.045)\tETA 0:00:57\tLoss 0.5324 (0.5317)\n",
      "Epoch: [5][765/792]\tTime 1.735 (1381.964)\tETA 0:00:46\tLoss 0.5329 (0.5317)\n",
      "Epoch: [5][770/792]\tTime 1.836 (1390.790)\tETA 0:00:40\tLoss 0.5300 (0.5317)\n",
      "Epoch: [5][775/792]\tTime 1.767 (1399.632)\tETA 0:00:30\tLoss 0.5323 (0.5317)\n",
      "Epoch: [5][780/792]\tTime 1.815 (1408.620)\tETA 0:00:21\tLoss 0.5302 (0.5317)\n",
      "Epoch: [5][785/792]\tTime 1.841 (1417.713)\tETA 0:00:12\tLoss 0.5330 (0.5317)\n",
      "Epoch: [5][790/792]\tTime 1.803 (1426.841)\tETA 0:00:03\tLoss 0.5310 (0.5317)\n",
      "Epoch: [6][0/792]\tTime 1.764 (1.764)\tETA 0:23:17\tLoss 0.5304 (0.5304)\n",
      "Epoch: [6][5/792]\tTime 1.825 (10.952)\tETA 0:23:56\tLoss 0.5303 (0.5310)\n",
      "Epoch: [6][10/792]\tTime 1.852 (20.154)\tETA 0:24:08\tLoss 0.5278 (0.5306)\n",
      "Epoch: [6][15/792]\tTime 1.785 (29.214)\tETA 0:23:06\tLoss 0.5338 (0.5310)\n",
      "Epoch: [6][20/792]\tTime 1.778 (38.093)\tETA 0:22:52\tLoss 0.5304 (0.5310)\n",
      "Epoch: [6][25/792]\tTime 1.728 (46.919)\tETA 0:22:05\tLoss 0.5345 (0.5311)\n",
      "Epoch: [6][30/792]\tTime 1.724 (55.651)\tETA 0:21:53\tLoss 0.5277 (0.5308)\n",
      "Epoch: [6][35/792]\tTime 1.721 (64.392)\tETA 0:21:43\tLoss 0.5278 (0.5306)\n",
      "Epoch: [6][40/792]\tTime 1.824 (73.303)\tETA 0:22:51\tLoss 0.5337 (0.5305)\n",
      "Epoch: [6][45/792]\tTime 1.757 (82.090)\tETA 0:21:52\tLoss 0.5359 (0.5306)\n",
      "Epoch: [6][50/792]\tTime 1.818 (90.945)\tETA 0:22:28\tLoss 0.5376 (0.5311)\n",
      "Epoch: [6][55/792]\tTime 1.802 (99.872)\tETA 0:22:08\tLoss 0.5291 (0.5312)\n",
      "Epoch: [6][60/792]\tTime 1.824 (108.836)\tETA 0:22:15\tLoss 0.5325 (0.5313)\n",
      "Epoch: [6][65/792]\tTime 1.741 (117.570)\tETA 0:21:05\tLoss 0.5293 (0.5311)\n",
      "Epoch: [6][70/792]\tTime 1.784 (126.580)\tETA 0:21:27\tLoss 0.5285 (0.5309)\n",
      "Epoch: [6][75/792]\tTime 1.807 (135.753)\tETA 0:21:35\tLoss 0.5318 (0.5308)\n",
      "Epoch: [6][80/792]\tTime 1.752 (144.726)\tETA 0:20:47\tLoss 0.5312 (0.5309)\n",
      "Epoch: [6][85/792]\tTime 1.773 (153.688)\tETA 0:20:53\tLoss 0.5318 (0.5310)\n",
      "Epoch: [6][90/792]\tTime 1.789 (162.593)\tETA 0:20:56\tLoss 0.5286 (0.5309)\n",
      "Epoch: [6][95/792]\tTime 1.783 (171.522)\tETA 0:20:42\tLoss 0.5319 (0.5308)\n",
      "Epoch: [6][100/792]\tTime 1.872 (180.672)\tETA 0:21:35\tLoss 0.5338 (0.5309)\n",
      "Epoch: [6][105/792]\tTime 1.844 (189.826)\tETA 0:21:06\tLoss 0.5309 (0.5309)\n",
      "Epoch: [6][110/792]\tTime 1.751 (198.584)\tETA 0:19:53\tLoss 0.5293 (0.5308)\n",
      "Epoch: [6][115/792]\tTime 1.768 (207.477)\tETA 0:19:56\tLoss 0.5306 (0.5308)\n",
      "Epoch: [6][120/792]\tTime 1.774 (216.400)\tETA 0:19:51\tLoss 0.5315 (0.5308)\n",
      "Epoch: [6][125/792]\tTime 1.738 (225.330)\tETA 0:19:19\tLoss 0.5270 (0.5307)\n",
      "Epoch: [6][130/792]\tTime 1.768 (234.503)\tETA 0:19:30\tLoss 0.5283 (0.5306)\n",
      "Epoch: [6][135/792]\tTime 1.800 (243.411)\tETA 0:19:42\tLoss 0.5272 (0.5305)\n",
      "Epoch: [6][140/792]\tTime 1.747 (252.467)\tETA 0:18:58\tLoss 0.5274 (0.5304)\n",
      "Epoch: [6][145/792]\tTime 1.802 (261.370)\tETA 0:19:26\tLoss 0.5281 (0.5303)\n",
      "Epoch: [6][150/792]\tTime 2.035 (270.586)\tETA 0:21:46\tLoss 0.5306 (0.5303)\n",
      "Epoch: [6][155/792]\tTime 1.841 (279.596)\tETA 0:19:32\tLoss 0.5284 (0.5304)\n",
      "Epoch: [6][160/792]\tTime 1.828 (288.653)\tETA 0:19:15\tLoss 0.5295 (0.5304)\n",
      "Epoch: [6][165/792]\tTime 1.754 (297.563)\tETA 0:18:19\tLoss 0.5293 (0.5304)\n",
      "Epoch: [6][170/792]\tTime 1.793 (306.535)\tETA 0:18:35\tLoss 0.5322 (0.5304)\n",
      "Epoch: [6][175/792]\tTime 1.868 (315.418)\tETA 0:19:12\tLoss 0.5273 (0.5304)\n",
      "Epoch: [6][180/792]\tTime 1.719 (324.307)\tETA 0:17:31\tLoss 0.5400 (0.5304)\n",
      "Epoch: [6][185/792]\tTime 1.727 (333.191)\tETA 0:17:28\tLoss 0.5307 (0.5305)\n",
      "Epoch: [6][190/792]\tTime 1.982 (342.306)\tETA 0:19:53\tLoss 0.5295 (0.5305)\n",
      "Epoch: [6][195/792]\tTime 1.796 (351.394)\tETA 0:17:52\tLoss 0.5270 (0.5304)\n",
      "Epoch: [6][200/792]\tTime 1.831 (360.709)\tETA 0:18:03\tLoss 0.5315 (0.5304)\n",
      "Epoch: [6][205/792]\tTime 1.838 (369.618)\tETA 0:17:59\tLoss 0.5310 (0.5303)\n",
      "Epoch: [6][210/792]\tTime 1.777 (378.602)\tETA 0:17:14\tLoss 0.5269 (0.5303)\n",
      "Epoch: [6][215/792]\tTime 1.741 (387.521)\tETA 0:16:44\tLoss 0.5319 (0.5303)\n",
      "Epoch: [6][220/792]\tTime 1.750 (396.483)\tETA 0:16:41\tLoss 0.5268 (0.5302)\n",
      "Epoch: [6][225/792]\tTime 1.858 (405.598)\tETA 0:17:33\tLoss 0.5246 (0.5302)\n",
      "Epoch: [6][230/792]\tTime 1.778 (414.486)\tETA 0:16:39\tLoss 0.5279 (0.5301)\n",
      "Epoch: [6][235/792]\tTime 1.765 (423.350)\tETA 0:16:23\tLoss 0.5315 (0.5301)\n",
      "Epoch: [6][240/792]\tTime 1.902 (432.475)\tETA 0:17:29\tLoss 0.5291 (0.5301)\n",
      "Epoch: [6][245/792]\tTime 1.805 (441.523)\tETA 0:16:27\tLoss 0.5293 (0.5300)\n",
      "Epoch: [6][250/792]\tTime 1.782 (450.647)\tETA 0:16:06\tLoss 0.5258 (0.5300)\n",
      "Epoch: [6][255/792]\tTime 1.781 (459.491)\tETA 0:15:56\tLoss 0.5299 (0.5300)\n",
      "Epoch: [6][260/792]\tTime 1.812 (468.498)\tETA 0:16:03\tLoss 0.5281 (0.5300)\n",
      "Epoch: [6][265/792]\tTime 1.750 (477.357)\tETA 0:15:22\tLoss 0.5328 (0.5300)\n",
      "Epoch: [6][270/792]\tTime 1.799 (486.267)\tETA 0:15:39\tLoss 0.5274 (0.5300)\n",
      "Epoch: [6][275/792]\tTime 1.829 (495.260)\tETA 0:15:45\tLoss 0.5344 (0.5300)\n",
      "Epoch: [6][280/792]\tTime 1.787 (504.073)\tETA 0:15:14\tLoss 0.5299 (0.5300)\n",
      "Epoch: [6][285/792]\tTime 1.784 (513.104)\tETA 0:15:04\tLoss 0.5306 (0.5299)\n",
      "Epoch: [6][290/792]\tTime 1.857 (522.005)\tETA 0:15:32\tLoss 0.5277 (0.5299)\n",
      "Epoch: [6][295/792]\tTime 1.756 (530.942)\tETA 0:14:32\tLoss 0.5275 (0.5299)\n",
      "Epoch: [6][300/792]\tTime 1.757 (539.784)\tETA 0:14:24\tLoss 0.5328 (0.5299)\n",
      "Epoch: [6][305/792]\tTime 1.769 (548.682)\tETA 0:14:21\tLoss 0.5288 (0.5299)\n",
      "Epoch: [6][310/792]\tTime 1.790 (557.648)\tETA 0:14:22\tLoss 0.5290 (0.5299)\n",
      "Epoch: [6][315/792]\tTime 1.741 (566.465)\tETA 0:13:50\tLoss 0.5277 (0.5299)\n",
      "Epoch: [6][320/792]\tTime 1.847 (575.458)\tETA 0:14:31\tLoss 0.5245 (0.5299)\n",
      "Epoch: [6][325/792]\tTime 1.875 (584.353)\tETA 0:14:35\tLoss 0.5321 (0.5300)\n",
      "Epoch: [6][330/792]\tTime 1.804 (593.324)\tETA 0:13:53\tLoss 0.5253 (0.5299)\n",
      "Epoch: [6][335/792]\tTime 1.742 (602.220)\tETA 0:13:16\tLoss 0.5334 (0.5299)\n",
      "Epoch: [6][340/792]\tTime 1.767 (611.033)\tETA 0:13:18\tLoss 0.5290 (0.5299)\n",
      "Epoch: [6][345/792]\tTime 1.743 (619.910)\tETA 0:12:59\tLoss 0.5351 (0.5299)\n",
      "Epoch: [6][350/792]\tTime 1.829 (628.919)\tETA 0:13:28\tLoss 0.5258 (0.5300)\n",
      "Epoch: [6][355/792]\tTime 1.751 (637.798)\tETA 0:12:45\tLoss 0.5292 (0.5300)\n",
      "Epoch: [6][360/792]\tTime 1.808 (646.732)\tETA 0:13:01\tLoss 0.5284 (0.5300)\n",
      "Epoch: [6][365/792]\tTime 1.790 (655.754)\tETA 0:12:44\tLoss 0.5377 (0.5300)\n",
      "Epoch: [6][370/792]\tTime 2.172 (665.095)\tETA 0:15:16\tLoss 0.5329 (0.5301)\n",
      "Epoch: [6][375/792]\tTime 1.799 (674.119)\tETA 0:12:30\tLoss 0.5363 (0.5301)\n",
      "Epoch: [6][380/792]\tTime 1.850 (683.062)\tETA 0:12:42\tLoss 0.5304 (0.5301)\n",
      "Epoch: [6][385/792]\tTime 1.714 (691.923)\tETA 0:11:37\tLoss 0.5254 (0.5301)\n",
      "Epoch: [6][390/792]\tTime 1.705 (700.671)\tETA 0:11:25\tLoss 0.5301 (0.5301)\n",
      "Epoch: [6][395/792]\tTime 1.665 (709.309)\tETA 0:11:01\tLoss 0.5338 (0.5301)\n",
      "Epoch: [6][400/792]\tTime 1.769 (718.226)\tETA 0:11:33\tLoss 0.5276 (0.5301)\n",
      "Epoch: [6][405/792]\tTime 1.861 (727.254)\tETA 0:12:00\tLoss 0.5305 (0.5301)\n",
      "Epoch: [6][410/792]\tTime 1.753 (736.021)\tETA 0:11:09\tLoss 0.5299 (0.5301)\n",
      "Epoch: [6][415/792]\tTime 1.824 (744.959)\tETA 0:11:27\tLoss 0.5277 (0.5301)\n",
      "Epoch: [6][420/792]\tTime 1.783 (753.790)\tETA 0:11:03\tLoss 0.5270 (0.5300)\n",
      "Epoch: [6][425/792]\tTime 1.713 (762.741)\tETA 0:10:28\tLoss 0.5301 (0.5300)\n",
      "Epoch: [6][430/792]\tTime 1.820 (771.671)\tETA 0:10:58\tLoss 0.5301 (0.5300)\n",
      "Epoch: [6][435/792]\tTime 1.762 (780.552)\tETA 0:10:29\tLoss 0.5288 (0.5300)\n",
      "Epoch: [6][440/792]\tTime 1.761 (789.265)\tETA 0:10:19\tLoss 0.5306 (0.5300)\n",
      "Epoch: [6][445/792]\tTime 1.743 (798.080)\tETA 0:10:04\tLoss 0.5293 (0.5300)\n",
      "Epoch: [6][450/792]\tTime 1.761 (806.984)\tETA 0:10:02\tLoss 0.5275 (0.5300)\n",
      "Epoch: [6][455/792]\tTime 1.786 (815.918)\tETA 0:10:02\tLoss 0.5303 (0.5300)\n",
      "Epoch: [6][460/792]\tTime 1.777 (824.693)\tETA 0:09:50\tLoss 0.5362 (0.5300)\n",
      "Epoch: [6][465/792]\tTime 1.755 (833.619)\tETA 0:09:34\tLoss 0.5335 (0.5300)\n",
      "Epoch: [6][470/792]\tTime 1.806 (842.422)\tETA 0:09:41\tLoss 0.5285 (0.5300)\n",
      "Epoch: [6][475/792]\tTime 1.747 (851.257)\tETA 0:09:13\tLoss 0.5286 (0.5300)\n",
      "Epoch: [6][480/792]\tTime 1.737 (860.165)\tETA 0:09:02\tLoss 0.5308 (0.5300)\n",
      "Epoch: [6][485/792]\tTime 1.768 (868.990)\tETA 0:09:02\tLoss 0.5293 (0.5300)\n",
      "Epoch: [6][490/792]\tTime 1.757 (877.814)\tETA 0:08:50\tLoss 0.5293 (0.5300)\n",
      "Epoch: [6][495/792]\tTime 1.704 (886.648)\tETA 0:08:26\tLoss 0.5280 (0.5300)\n",
      "Epoch: [6][500/792]\tTime 1.731 (895.436)\tETA 0:08:25\tLoss 0.5301 (0.5299)\n",
      "Epoch: [6][505/792]\tTime 1.756 (904.405)\tETA 0:08:23\tLoss 0.5302 (0.5299)\n",
      "Epoch: [6][510/792]\tTime 1.690 (913.250)\tETA 0:07:56\tLoss 0.5297 (0.5299)\n",
      "Epoch: [6][515/792]\tTime 1.824 (922.197)\tETA 0:08:25\tLoss 0.5252 (0.5299)\n",
      "Epoch: [6][520/792]\tTime 1.762 (931.041)\tETA 0:07:59\tLoss 0.5270 (0.5299)\n",
      "Epoch: [6][525/792]\tTime 1.738 (939.871)\tETA 0:07:44\tLoss 0.5294 (0.5299)\n",
      "Epoch: [6][530/792]\tTime 1.778 (948.761)\tETA 0:07:45\tLoss 0.5301 (0.5299)\n",
      "Epoch: [6][535/792]\tTime 1.834 (957.691)\tETA 0:07:51\tLoss 0.5294 (0.5299)\n",
      "Epoch: [6][540/792]\tTime 1.789 (966.532)\tETA 0:07:30\tLoss 0.5317 (0.5299)\n",
      "Epoch: [6][545/792]\tTime 1.792 (975.635)\tETA 0:07:22\tLoss 0.5294 (0.5299)\n",
      "Epoch: [6][550/792]\tTime 1.828 (984.689)\tETA 0:07:22\tLoss 0.5283 (0.5299)\n",
      "Epoch: [6][555/792]\tTime 1.781 (993.539)\tETA 0:07:02\tLoss 0.5277 (0.5299)\n",
      "Epoch: [6][560/792]\tTime 1.756 (1002.542)\tETA 0:06:47\tLoss 0.5304 (0.5299)\n",
      "Epoch: [6][565/792]\tTime 1.754 (1011.367)\tETA 0:06:38\tLoss 0.5315 (0.5299)\n",
      "Epoch: [6][570/792]\tTime 1.806 (1020.287)\tETA 0:06:41\tLoss 0.5284 (0.5299)\n",
      "Epoch: [6][575/792]\tTime 1.845 (1029.455)\tETA 0:06:40\tLoss 0.5388 (0.5300)\n",
      "Epoch: [6][580/792]\tTime 1.726 (1038.240)\tETA 0:06:06\tLoss 0.5319 (0.5300)\n",
      "Epoch: [6][585/792]\tTime 1.793 (1047.145)\tETA 0:06:11\tLoss 0.5347 (0.5300)\n",
      "Epoch: [6][590/792]\tTime 1.830 (1056.034)\tETA 0:06:09\tLoss 0.5253 (0.5300)\n",
      "Epoch: [6][595/792]\tTime 1.757 (1064.842)\tETA 0:05:46\tLoss 0.5305 (0.5300)\n",
      "Epoch: [6][600/792]\tTime 1.761 (1073.718)\tETA 0:05:38\tLoss 0.5293 (0.5300)\n",
      "Epoch: [6][605/792]\tTime 1.800 (1082.775)\tETA 0:05:36\tLoss 0.5288 (0.5300)\n",
      "Epoch: [6][610/792]\tTime 1.784 (1091.671)\tETA 0:05:24\tLoss 0.5339 (0.5300)\n",
      "Epoch: [6][615/792]\tTime 1.710 (1100.626)\tETA 0:05:02\tLoss 0.5310 (0.5300)\n",
      "Epoch: [6][620/792]\tTime 1.740 (1109.451)\tETA 0:04:59\tLoss 0.5283 (0.5300)\n",
      "Epoch: [6][625/792]\tTime 1.804 (1118.550)\tETA 0:05:01\tLoss 0.5259 (0.5300)\n",
      "Epoch: [6][630/792]\tTime 1.756 (1127.395)\tETA 0:04:44\tLoss 0.5314 (0.5300)\n",
      "Epoch: [6][635/792]\tTime 1.710 (1136.125)\tETA 0:04:28\tLoss 0.5289 (0.5299)\n",
      "Epoch: [6][640/792]\tTime 1.829 (1145.219)\tETA 0:04:38\tLoss 0.5317 (0.5299)\n",
      "Epoch: [6][645/792]\tTime 1.776 (1154.369)\tETA 0:04:21\tLoss 0.5305 (0.5300)\n",
      "Epoch: [6][650/792]\tTime 1.847 (1163.254)\tETA 0:04:22\tLoss 0.5286 (0.5299)\n",
      "Epoch: [6][655/792]\tTime 1.774 (1171.986)\tETA 0:04:03\tLoss 0.5272 (0.5299)\n",
      "Epoch: [6][660/792]\tTime 1.746 (1180.878)\tETA 0:03:50\tLoss 0.5262 (0.5299)\n",
      "Epoch: [6][665/792]\tTime 1.819 (1189.751)\tETA 0:03:50\tLoss 0.5246 (0.5299)\n",
      "Epoch: [6][670/792]\tTime 1.785 (1198.999)\tETA 0:03:37\tLoss 0.5293 (0.5299)\n",
      "Epoch: [6][675/792]\tTime 1.802 (1208.251)\tETA 0:03:30\tLoss 0.5299 (0.5299)\n",
      "Epoch: [6][680/792]\tTime 1.758 (1217.165)\tETA 0:03:16\tLoss 0.5287 (0.5299)\n",
      "Epoch: [6][685/792]\tTime 1.843 (1226.153)\tETA 0:03:17\tLoss 0.5281 (0.5299)\n",
      "Epoch: [6][690/792]\tTime 1.787 (1235.209)\tETA 0:03:02\tLoss 0.5314 (0.5299)\n",
      "Epoch: [6][695/792]\tTime 1.792 (1244.214)\tETA 0:02:53\tLoss 0.5265 (0.5299)\n",
      "Epoch: [6][700/792]\tTime 1.726 (1253.059)\tETA 0:02:38\tLoss 0.5290 (0.5299)\n",
      "Epoch: [6][705/792]\tTime 1.750 (1261.834)\tETA 0:02:32\tLoss 0.5311 (0.5299)\n",
      "Epoch: [6][710/792]\tTime 1.825 (1270.680)\tETA 0:02:29\tLoss 0.5313 (0.5299)\n",
      "Epoch: [6][715/792]\tTime 1.764 (1279.489)\tETA 0:02:15\tLoss 0.5281 (0.5299)\n",
      "Epoch: [6][720/792]\tTime 1.754 (1288.646)\tETA 0:02:06\tLoss 0.5297 (0.5298)\n",
      "Epoch: [6][725/792]\tTime 1.769 (1297.447)\tETA 0:01:58\tLoss 0.5307 (0.5298)\n",
      "Epoch: [6][730/792]\tTime 1.711 (1306.178)\tETA 0:01:46\tLoss 0.5314 (0.5298)\n",
      "Epoch: [6][735/792]\tTime 1.799 (1315.088)\tETA 0:01:42\tLoss 0.5298 (0.5298)\n",
      "Epoch: [6][740/792]\tTime 1.859 (1324.036)\tETA 0:01:36\tLoss 0.5239 (0.5298)\n",
      "Epoch: [6][745/792]\tTime 1.744 (1333.046)\tETA 0:01:21\tLoss 0.5283 (0.5298)\n",
      "Epoch: [6][750/792]\tTime 1.836 (1342.005)\tETA 0:01:17\tLoss 0.5291 (0.5298)\n",
      "Epoch: [6][755/792]\tTime 1.832 (1351.214)\tETA 0:01:07\tLoss 0.5269 (0.5298)\n",
      "Epoch: [6][760/792]\tTime 1.791 (1359.858)\tETA 0:00:57\tLoss 0.5292 (0.5298)\n",
      "Epoch: [6][765/792]\tTime 1.805 (1368.711)\tETA 0:00:48\tLoss 0.5269 (0.5298)\n",
      "Epoch: [6][770/792]\tTime 1.789 (1377.676)\tETA 0:00:39\tLoss 0.5264 (0.5298)\n",
      "Epoch: [6][775/792]\tTime 1.750 (1386.676)\tETA 0:00:29\tLoss 0.5311 (0.5298)\n",
      "Epoch: [6][780/792]\tTime 1.787 (1395.610)\tETA 0:00:21\tLoss 0.5246 (0.5297)\n",
      "Epoch: [6][785/792]\tTime 1.812 (1404.457)\tETA 0:00:12\tLoss 0.5270 (0.5297)\n",
      "Epoch: [6][790/792]\tTime 1.778 (1413.355)\tETA 0:00:03\tLoss 0.5299 (0.5297)\n",
      "Epoch: [7][0/792]\tTime 1.749 (1.749)\tETA 0:23:05\tLoss 0.5267 (0.5267)\n",
      "Epoch: [7][5/792]\tTime 1.735 (10.618)\tETA 0:22:45\tLoss 0.5286 (0.5271)\n",
      "Epoch: [7][10/792]\tTime 1.776 (19.590)\tETA 0:23:09\tLoss 0.5261 (0.5270)\n",
      "Epoch: [7][15/792]\tTime 1.795 (28.575)\tETA 0:23:14\tLoss 0.5308 (0.5274)\n",
      "Epoch: [7][20/792]\tTime 1.774 (37.642)\tETA 0:22:49\tLoss 0.5290 (0.5275)\n",
      "Epoch: [7][25/792]\tTime 1.811 (46.530)\tETA 0:23:08\tLoss 0.5272 (0.5276)\n",
      "Epoch: [7][30/792]\tTime 1.708 (55.390)\tETA 0:21:41\tLoss 0.5258 (0.5276)\n",
      "Epoch: [7][35/792]\tTime 1.782 (64.505)\tETA 0:22:28\tLoss 0.5273 (0.5275)\n",
      "Epoch: [7][40/792]\tTime 1.752 (73.253)\tETA 0:21:57\tLoss 0.5285 (0.5276)\n",
      "Epoch: [7][45/792]\tTime 1.706 (82.244)\tETA 0:21:14\tLoss 0.5306 (0.5276)\n",
      "Epoch: [7][50/792]\tTime 1.708 (91.058)\tETA 0:21:07\tLoss 0.5275 (0.5276)\n",
      "Epoch: [7][55/792]\tTime 1.779 (99.792)\tETA 0:21:50\tLoss 0.5305 (0.5278)\n",
      "Epoch: [7][60/792]\tTime 1.777 (108.648)\tETA 0:21:40\tLoss 0.5308 (0.5278)\n",
      "Epoch: [7][65/792]\tTime 1.732 (117.474)\tETA 0:20:59\tLoss 0.5263 (0.5277)\n",
      "Epoch: [7][70/792]\tTime 1.852 (126.379)\tETA 0:22:16\tLoss 0.5261 (0.5277)\n",
      "Epoch: [7][75/792]\tTime 1.767 (135.517)\tETA 0:21:06\tLoss 0.5259 (0.5277)\n",
      "Epoch: [7][80/792]\tTime 1.738 (144.566)\tETA 0:20:37\tLoss 0.5272 (0.5277)\n",
      "Epoch: [7][85/792]\tTime 1.788 (153.397)\tETA 0:21:04\tLoss 0.5284 (0.5277)\n",
      "Epoch: [7][90/792]\tTime 1.845 (162.326)\tETA 0:21:35\tLoss 0.5276 (0.5278)\n",
      "Epoch: [7][95/792]\tTime 1.782 (171.223)\tETA 0:20:41\tLoss 0.5382 (0.5281)\n",
      "Epoch: [7][100/792]\tTime 1.774 (180.004)\tETA 0:20:27\tLoss 0.5305 (0.5281)\n",
      "Epoch: [7][105/792]\tTime 1.785 (188.770)\tETA 0:20:26\tLoss 0.5300 (0.5282)\n",
      "Epoch: [7][110/792]\tTime 1.771 (197.723)\tETA 0:20:07\tLoss 0.5294 (0.5282)\n",
      "Epoch: [7][115/792]\tTime 2.012 (206.937)\tETA 0:22:42\tLoss 0.5249 (0.5281)\n",
      "Epoch: [7][120/792]\tTime 1.751 (215.969)\tETA 0:19:36\tLoss 0.5255 (0.5281)\n",
      "Epoch: [7][125/792]\tTime 1.726 (224.757)\tETA 0:19:11\tLoss 0.5254 (0.5280)\n",
      "Epoch: [7][130/792]\tTime 1.787 (233.881)\tETA 0:19:43\tLoss 0.5270 (0.5280)\n",
      "Epoch: [7][135/792]\tTime 1.742 (243.008)\tETA 0:19:04\tLoss 0.5254 (0.5279)\n",
      "Epoch: [7][140/792]\tTime 1.811 (251.865)\tETA 0:19:40\tLoss 0.5271 (0.5279)\n",
      "Epoch: [7][145/792]\tTime 1.780 (260.798)\tETA 0:19:11\tLoss 0.5277 (0.5279)\n",
      "Epoch: [7][150/792]\tTime 1.804 (269.683)\tETA 0:19:18\tLoss 0.5333 (0.5279)\n",
      "Epoch: [7][155/792]\tTime 1.758 (278.598)\tETA 0:18:39\tLoss 0.5303 (0.5280)\n",
      "Epoch: [7][160/792]\tTime 1.761 (287.404)\tETA 0:18:32\tLoss 0.5314 (0.5281)\n",
      "Epoch: [7][165/792]\tTime 1.933 (296.760)\tETA 0:20:12\tLoss 0.5274 (0.5282)\n",
      "Epoch: [7][170/792]\tTime 1.734 (305.626)\tETA 0:17:58\tLoss 0.5292 (0.5282)\n",
      "Epoch: [7][175/792]\tTime 1.723 (314.812)\tETA 0:17:43\tLoss 0.5289 (0.5282)\n",
      "Epoch: [7][180/792]\tTime 1.837 (323.674)\tETA 0:18:44\tLoss 0.5328 (0.5283)\n",
      "Epoch: [7][185/792]\tTime 1.791 (332.920)\tETA 0:18:06\tLoss 0.5298 (0.5283)\n",
      "Epoch: [7][190/792]\tTime 1.761 (341.862)\tETA 0:17:40\tLoss 0.5302 (0.5284)\n",
      "Epoch: [7][195/792]\tTime 1.742 (350.608)\tETA 0:17:20\tLoss 0.5347 (0.5286)\n",
      "Epoch: [7][200/792]\tTime 1.799 (359.605)\tETA 0:17:45\tLoss 0.5347 (0.5286)\n",
      "Epoch: [7][205/792]\tTime 1.765 (368.544)\tETA 0:17:16\tLoss 0.5246 (0.5287)\n",
      "Epoch: [7][210/792]\tTime 1.766 (377.563)\tETA 0:17:07\tLoss 0.5299 (0.5287)\n",
      "Epoch: [7][215/792]\tTime 1.819 (386.716)\tETA 0:17:29\tLoss 0.5337 (0.5287)\n",
      "Epoch: [7][220/792]\tTime 1.733 (395.777)\tETA 0:16:31\tLoss 0.5324 (0.5287)\n",
      "Epoch: [7][225/792]\tTime 1.743 (404.885)\tETA 0:16:28\tLoss 0.5308 (0.5288)\n",
      "Epoch: [7][230/792]\tTime 1.682 (413.677)\tETA 0:15:45\tLoss 0.5271 (0.5287)\n",
      "Epoch: [7][235/792]\tTime 1.793 (422.764)\tETA 0:16:38\tLoss 0.5266 (0.5288)\n",
      "Epoch: [7][240/792]\tTime 1.818 (431.796)\tETA 0:16:43\tLoss 0.5283 (0.5288)\n",
      "Epoch: [7][245/792]\tTime 2.185 (441.093)\tETA 0:19:55\tLoss 0.5326 (0.5288)\n",
      "Epoch: [7][250/792]\tTime 1.798 (450.051)\tETA 0:16:14\tLoss 0.5260 (0.5287)\n",
      "Epoch: [7][255/792]\tTime 1.835 (459.064)\tETA 0:16:25\tLoss 0.5287 (0.5287)\n",
      "Epoch: [7][260/792]\tTime 1.782 (468.217)\tETA 0:15:48\tLoss 0.5255 (0.5287)\n",
      "Epoch: [7][265/792]\tTime 1.855 (477.430)\tETA 0:16:17\tLoss 0.5276 (0.5287)\n",
      "Epoch: [7][270/792]\tTime 1.833 (486.830)\tETA 0:15:56\tLoss 0.5275 (0.5287)\n",
      "Epoch: [7][275/792]\tTime 1.792 (495.879)\tETA 0:15:26\tLoss 0.5328 (0.5287)\n",
      "Epoch: [7][280/792]\tTime 1.825 (504.957)\tETA 0:15:34\tLoss 0.5257 (0.5286)\n",
      "Epoch: [7][285/792]\tTime 1.690 (513.875)\tETA 0:14:16\tLoss 0.5291 (0.5286)\n",
      "Epoch: [7][290/792]\tTime 1.763 (522.754)\tETA 0:14:45\tLoss 0.5259 (0.5286)\n",
      "Epoch: [7][295/792]\tTime 1.746 (531.507)\tETA 0:14:27\tLoss 0.5286 (0.5286)\n",
      "Epoch: [7][300/792]\tTime 1.792 (540.239)\tETA 0:14:41\tLoss 0.5294 (0.5286)\n",
      "Epoch: [7][305/792]\tTime 1.747 (549.023)\tETA 0:14:10\tLoss 0.5245 (0.5286)\n",
      "Epoch: [7][310/792]\tTime 1.704 (557.785)\tETA 0:13:41\tLoss 0.5290 (0.5286)\n",
      "Epoch: [7][315/792]\tTime 1.770 (566.730)\tETA 0:14:04\tLoss 0.5271 (0.5286)\n",
      "Epoch: [7][320/792]\tTime 1.767 (575.520)\tETA 0:13:54\tLoss 0.5313 (0.5285)\n",
      "Epoch: [7][325/792]\tTime 1.770 (584.305)\tETA 0:13:46\tLoss 0.5247 (0.5285)\n",
      "Epoch: [7][330/792]\tTime 1.759 (593.220)\tETA 0:13:32\tLoss 0.5368 (0.5286)\n",
      "Epoch: [7][335/792]\tTime 1.716 (602.044)\tETA 0:13:04\tLoss 0.5409 (0.5287)\n",
      "Epoch: [7][340/792]\tTime 1.766 (610.969)\tETA 0:13:18\tLoss 0.5315 (0.5287)\n",
      "Epoch: [7][345/792]\tTime 1.755 (620.394)\tETA 0:13:04\tLoss 0.5311 (0.5288)\n",
      "Epoch: [7][350/792]\tTime 2.062 (629.714)\tETA 0:15:11\tLoss 0.5312 (0.5288)\n",
      "Epoch: [7][355/792]\tTime 1.837 (638.652)\tETA 0:13:22\tLoss 0.5260 (0.5288)\n",
      "Epoch: [7][360/792]\tTime 1.709 (647.389)\tETA 0:12:18\tLoss 0.5289 (0.5288)\n",
      "Epoch: [7][365/792]\tTime 1.797 (656.253)\tETA 0:12:47\tLoss 0.5297 (0.5287)\n",
      "Epoch: [7][370/792]\tTime 1.805 (665.147)\tETA 0:12:41\tLoss 0.5299 (0.5288)\n",
      "Epoch: [7][375/792]\tTime 1.787 (673.929)\tETA 0:12:25\tLoss 0.5328 (0.5288)\n",
      "Epoch: [7][380/792]\tTime 1.761 (682.780)\tETA 0:12:05\tLoss 0.5289 (0.5288)\n",
      "Epoch: [7][385/792]\tTime 1.830 (691.702)\tETA 0:12:24\tLoss 0.5276 (0.5288)\n",
      "Epoch: [7][390/792]\tTime 1.784 (700.537)\tETA 0:11:57\tLoss 0.5265 (0.5287)\n",
      "Epoch: [7][395/792]\tTime 1.754 (709.462)\tETA 0:11:36\tLoss 0.5264 (0.5287)\n",
      "Epoch: [7][400/792]\tTime 1.786 (718.376)\tETA 0:11:39\tLoss 0.5311 (0.5287)\n",
      "Epoch: [7][405/792]\tTime 1.787 (727.153)\tETA 0:11:31\tLoss 0.5287 (0.5287)\n",
      "Epoch: [7][410/792]\tTime 1.721 (735.837)\tETA 0:10:57\tLoss 0.5254 (0.5287)\n",
      "Epoch: [7][415/792]\tTime 1.884 (744.947)\tETA 0:11:50\tLoss 0.5316 (0.5287)\n",
      "Epoch: [7][420/792]\tTime 1.800 (753.809)\tETA 0:11:09\tLoss 0.5265 (0.5287)\n",
      "Epoch: [7][425/792]\tTime 1.800 (762.794)\tETA 0:11:00\tLoss 0.5319 (0.5287)\n",
      "Epoch: [7][430/792]\tTime 1.788 (771.857)\tETA 0:10:47\tLoss 0.5295 (0.5287)\n",
      "Epoch: [7][435/792]\tTime 1.808 (781.266)\tETA 0:10:45\tLoss 0.5314 (0.5287)\n",
      "Epoch: [7][440/792]\tTime 1.767 (790.190)\tETA 0:10:21\tLoss 0.5268 (0.5287)\n",
      "Epoch: [7][445/792]\tTime 1.811 (799.330)\tETA 0:10:28\tLoss 0.5287 (0.5287)\n",
      "Epoch: [7][450/792]\tTime 1.729 (808.204)\tETA 0:09:51\tLoss 0.5281 (0.5287)\n",
      "Epoch: [7][455/792]\tTime 1.837 (817.548)\tETA 0:10:18\tLoss 0.5274 (0.5287)\n",
      "Epoch: [7][460/792]\tTime 1.785 (826.369)\tETA 0:09:52\tLoss 0.5266 (0.5287)\n",
      "Epoch: [7][465/792]\tTime 1.823 (835.326)\tETA 0:09:56\tLoss 0.5261 (0.5286)\n",
      "Epoch: [7][470/792]\tTime 1.786 (844.167)\tETA 0:09:35\tLoss 0.5316 (0.5286)\n",
      "Epoch: [7][475/792]\tTime 1.777 (852.988)\tETA 0:09:23\tLoss 0.5285 (0.5286)\n",
      "Epoch: [7][480/792]\tTime 1.806 (861.828)\tETA 0:09:23\tLoss 0.5253 (0.5286)\n",
      "Epoch: [7][485/792]\tTime 1.826 (870.853)\tETA 0:09:20\tLoss 0.5287 (0.5286)\n",
      "Epoch: [7][490/792]\tTime 1.786 (879.840)\tETA 0:08:59\tLoss 0.5282 (0.5286)\n",
      "Epoch: [7][495/792]\tTime 2.124 (888.972)\tETA 0:10:30\tLoss 0.5333 (0.5286)\n",
      "Epoch: [7][500/792]\tTime 1.790 (897.890)\tETA 0:08:42\tLoss 0.5261 (0.5286)\n",
      "Epoch: [7][505/792]\tTime 1.706 (906.638)\tETA 0:08:09\tLoss 0.5270 (0.5287)\n",
      "Epoch: [7][510/792]\tTime 1.724 (915.480)\tETA 0:08:06\tLoss 0.5290 (0.5287)\n",
      "Epoch: [7][515/792]\tTime 1.919 (924.589)\tETA 0:08:51\tLoss 0.5307 (0.5287)\n",
      "Epoch: [7][520/792]\tTime 1.837 (933.849)\tETA 0:08:19\tLoss 0.5302 (0.5287)\n",
      "Epoch: [7][525/792]\tTime 1.723 (942.719)\tETA 0:07:40\tLoss 0.5274 (0.5287)\n",
      "Epoch: [7][530/792]\tTime 1.728 (951.387)\tETA 0:07:32\tLoss 0.5268 (0.5287)\n",
      "Epoch: [7][535/792]\tTime 1.765 (960.322)\tETA 0:07:33\tLoss 0.5266 (0.5287)\n",
      "Epoch: [7][540/792]\tTime 1.768 (969.160)\tETA 0:07:25\tLoss 0.5301 (0.5287)\n",
      "Epoch: [7][545/792]\tTime 1.777 (978.007)\tETA 0:07:18\tLoss 0.5309 (0.5287)\n",
      "Epoch: [7][550/792]\tTime 1.774 (986.894)\tETA 0:07:09\tLoss 0.5275 (0.5287)\n",
      "Epoch: [7][555/792]\tTime 1.760 (995.809)\tETA 0:06:57\tLoss 0.5271 (0.5287)\n",
      "Epoch: [7][560/792]\tTime 1.719 (1004.618)\tETA 0:06:38\tLoss 0.5326 (0.5287)\n",
      "Epoch: [7][565/792]\tTime 1.779 (1013.400)\tETA 0:06:43\tLoss 0.5270 (0.5287)\n",
      "Epoch: [7][570/792]\tTime 1.814 (1022.221)\tETA 0:06:42\tLoss 0.5279 (0.5286)\n",
      "Epoch: [7][575/792]\tTime 1.780 (1031.101)\tETA 0:06:26\tLoss 0.5281 (0.5286)\n",
      "Epoch: [7][580/792]\tTime 1.844 (1040.193)\tETA 0:06:30\tLoss 0.5278 (0.5286)\n",
      "Epoch: [7][585/792]\tTime 1.809 (1049.039)\tETA 0:06:14\tLoss 0.5284 (0.5286)\n",
      "Epoch: [7][590/792]\tTime 1.755 (1057.918)\tETA 0:05:54\tLoss 0.5273 (0.5286)\n",
      "Epoch: [7][595/792]\tTime 1.755 (1066.843)\tETA 0:05:45\tLoss 0.5274 (0.5286)\n",
      "Epoch: [7][600/792]\tTime 1.829 (1075.725)\tETA 0:05:51\tLoss 0.5287 (0.5286)\n",
      "Epoch: [7][605/792]\tTime 1.837 (1084.627)\tETA 0:05:43\tLoss 0.5291 (0.5286)\n",
      "Epoch: [7][610/792]\tTime 1.745 (1093.670)\tETA 0:05:17\tLoss 0.5264 (0.5286)\n",
      "Epoch: [7][615/792]\tTime 1.775 (1102.563)\tETA 0:05:14\tLoss 0.5287 (0.5286)\n",
      "Epoch: [7][620/792]\tTime 1.772 (1111.497)\tETA 0:05:04\tLoss 0.5245 (0.5285)\n",
      "Epoch: [7][625/792]\tTime 1.740 (1121.043)\tETA 0:04:50\tLoss 0.5291 (0.5286)\n",
      "Epoch: [7][630/792]\tTime 1.755 (1130.147)\tETA 0:04:44\tLoss 0.5286 (0.5285)\n",
      "Epoch: [7][635/792]\tTime 1.739 (1139.065)\tETA 0:04:33\tLoss 0.5268 (0.5285)\n",
      "Epoch: [7][640/792]\tTime 1.896 (1148.053)\tETA 0:04:48\tLoss 0.5264 (0.5285)\n",
      "Epoch: [7][645/792]\tTime 1.786 (1157.203)\tETA 0:04:22\tLoss 0.5251 (0.5285)\n",
      "Epoch: [7][650/792]\tTime 1.905 (1166.423)\tETA 0:04:30\tLoss 0.5279 (0.5285)\n",
      "Epoch: [7][655/792]\tTime 1.759 (1175.370)\tETA 0:04:01\tLoss 0.5260 (0.5285)\n",
      "Epoch: [7][660/792]\tTime 1.947 (1184.551)\tETA 0:04:16\tLoss 0.5255 (0.5285)\n",
      "Epoch: [7][665/792]\tTime 1.725 (1193.338)\tETA 0:03:39\tLoss 0.5349 (0.5285)\n",
      "Epoch: [7][670/792]\tTime 1.822 (1202.310)\tETA 0:03:42\tLoss 0.5272 (0.5285)\n",
      "Epoch: [7][675/792]\tTime 1.792 (1211.137)\tETA 0:03:29\tLoss 0.5292 (0.5285)\n",
      "Epoch: [7][680/792]\tTime 1.841 (1220.018)\tETA 0:03:26\tLoss 0.5282 (0.5285)\n",
      "Epoch: [7][685/792]\tTime 1.838 (1229.221)\tETA 0:03:16\tLoss 0.5301 (0.5285)\n",
      "Epoch: [7][690/792]\tTime 1.791 (1238.608)\tETA 0:03:02\tLoss 0.5280 (0.5285)\n",
      "Epoch: [7][695/792]\tTime 1.844 (1248.227)\tETA 0:02:58\tLoss 0.5298 (0.5285)\n",
      "Epoch: [7][700/792]\tTime 1.743 (1256.997)\tETA 0:02:40\tLoss 0.5259 (0.5285)\n",
      "Epoch: [7][705/792]\tTime 1.811 (1266.047)\tETA 0:02:37\tLoss 0.5257 (0.5285)\n",
      "Epoch: [7][710/792]\tTime 1.738 (1275.273)\tETA 0:02:22\tLoss 0.5280 (0.5284)\n",
      "Epoch: [7][715/792]\tTime 1.759 (1284.140)\tETA 0:02:15\tLoss 0.5279 (0.5284)\n",
      "Epoch: [7][720/792]\tTime 1.739 (1293.009)\tETA 0:02:05\tLoss 0.5298 (0.5284)\n",
      "Epoch: [7][725/792]\tTime 1.787 (1301.898)\tETA 0:01:59\tLoss 0.5269 (0.5284)\n",
      "Epoch: [7][730/792]\tTime 1.835 (1311.521)\tETA 0:01:53\tLoss 0.5295 (0.5285)\n",
      "Epoch: [7][735/792]\tTime 1.741 (1320.309)\tETA 0:01:39\tLoss 0.5318 (0.5285)\n",
      "Epoch: [7][740/792]\tTime 1.807 (1329.019)\tETA 0:01:33\tLoss 0.5290 (0.5285)\n",
      "Epoch: [7][745/792]\tTime 1.828 (1338.115)\tETA 0:01:25\tLoss 0.5272 (0.5285)\n",
      "Epoch: [7][750/792]\tTime 1.742 (1346.989)\tETA 0:01:13\tLoss 0.5297 (0.5285)\n",
      "Epoch: [7][755/792]\tTime 1.762 (1355.920)\tETA 0:01:05\tLoss 0.5285 (0.5285)\n",
      "Epoch: [7][760/792]\tTime 1.758 (1365.017)\tETA 0:00:56\tLoss 0.5291 (0.5285)\n",
      "Epoch: [7][765/792]\tTime 1.852 (1374.289)\tETA 0:00:49\tLoss 0.5251 (0.5285)\n",
      "Epoch: [7][770/792]\tTime 1.807 (1383.292)\tETA 0:00:39\tLoss 0.5289 (0.5285)\n",
      "Epoch: [7][775/792]\tTime 1.878 (1392.136)\tETA 0:00:31\tLoss 0.5265 (0.5285)\n",
      "Epoch: [7][780/792]\tTime 1.706 (1400.913)\tETA 0:00:20\tLoss 0.5237 (0.5284)\n",
      "Epoch: [7][785/792]\tTime 1.756 (1409.787)\tETA 0:00:12\tLoss 0.5291 (0.5284)\n",
      "Epoch: [7][790/792]\tTime 1.770 (1418.610)\tETA 0:00:03\tLoss 0.5313 (0.5285)\n",
      "Epoch: [8][0/792]\tTime 1.883 (1.883)\tETA 0:24:51\tLoss 0.5308 (0.5308)\n",
      "Epoch: [8][5/792]\tTime 1.819 (10.706)\tETA 0:23:51\tLoss 0.5270 (0.5278)\n",
      "Epoch: [8][10/792]\tTime 1.807 (19.667)\tETA 0:23:32\tLoss 0.5280 (0.5274)\n",
      "Epoch: [8][15/792]\tTime 1.775 (28.509)\tETA 0:22:59\tLoss 0.5256 (0.5273)\n",
      "Epoch: [8][20/792]\tTime 1.731 (37.335)\tETA 0:22:16\tLoss 0.5277 (0.5270)\n",
      "Epoch: [8][25/792]\tTime 1.792 (46.183)\tETA 0:22:54\tLoss 0.5287 (0.5270)\n",
      "Epoch: [8][30/792]\tTime 1.775 (55.014)\tETA 0:22:32\tLoss 0.5261 (0.5268)\n",
      "Epoch: [8][35/792]\tTime 1.789 (63.950)\tETA 0:22:34\tLoss 0.5305 (0.5273)\n",
      "Epoch: [8][40/792]\tTime 1.743 (72.766)\tETA 0:21:50\tLoss 0.5291 (0.5273)\n",
      "Epoch: [8][45/792]\tTime 1.810 (81.618)\tETA 0:22:32\tLoss 0.5273 (0.5272)\n",
      "Epoch: [8][50/792]\tTime 1.748 (90.430)\tETA 0:21:37\tLoss 0.5300 (0.5272)\n",
      "Epoch: [8][55/792]\tTime 1.781 (99.394)\tETA 0:21:52\tLoss 0.5286 (0.5273)\n",
      "Epoch: [8][60/792]\tTime 1.808 (108.182)\tETA 0:22:03\tLoss 0.5283 (0.5274)\n",
      "Epoch: [8][65/792]\tTime 1.762 (117.013)\tETA 0:21:21\tLoss 0.5239 (0.5273)\n",
      "Epoch: [8][70/792]\tTime 1.782 (125.872)\tETA 0:21:26\tLoss 0.5243 (0.5273)\n",
      "Epoch: [8][75/792]\tTime 1.795 (134.735)\tETA 0:21:27\tLoss 0.5262 (0.5274)\n",
      "Epoch: [8][80/792]\tTime 1.784 (143.444)\tETA 0:21:10\tLoss 0.5283 (0.5274)\n",
      "Epoch: [8][85/792]\tTime 1.664 (152.118)\tETA 0:19:36\tLoss 0.5292 (0.5275)\n",
      "Epoch: [8][90/792]\tTime 1.769 (160.693)\tETA 0:20:42\tLoss 0.5324 (0.5276)\n",
      "Epoch: [8][95/792]\tTime 1.758 (169.596)\tETA 0:20:25\tLoss 0.5271 (0.5277)\n",
      "Epoch: [8][100/792]\tTime 1.795 (178.483)\tETA 0:20:41\tLoss 0.5250 (0.5276)\n",
      "Epoch: [8][105/792]\tTime 1.786 (187.365)\tETA 0:20:26\tLoss 0.5272 (0.5276)\n",
      "Epoch: [8][110/792]\tTime 1.773 (196.230)\tETA 0:20:09\tLoss 0.5251 (0.5275)\n",
      "Epoch: [8][115/792]\tTime 1.763 (205.153)\tETA 0:19:53\tLoss 0.5308 (0.5275)\n",
      "Epoch: [8][120/792]\tTime 1.795 (214.000)\tETA 0:20:06\tLoss 0.5259 (0.5275)\n",
      "Epoch: [8][125/792]\tTime 1.869 (222.950)\tETA 0:20:46\tLoss 0.5264 (0.5275)\n",
      "Epoch: [8][130/792]\tTime 1.743 (232.278)\tETA 0:19:13\tLoss 0.5262 (0.5275)\n",
      "Epoch: [8][135/792]\tTime 1.781 (241.221)\tETA 0:19:29\tLoss 0.5248 (0.5274)\n",
      "Epoch: [8][140/792]\tTime 1.791 (249.984)\tETA 0:19:27\tLoss 0.5258 (0.5275)\n",
      "Epoch: [8][145/792]\tTime 1.785 (258.841)\tETA 0:19:14\tLoss 0.5325 (0.5275)\n",
      "Epoch: [8][150/792]\tTime 1.751 (267.707)\tETA 0:18:44\tLoss 0.5294 (0.5276)\n",
      "Epoch: [8][155/792]\tTime 1.734 (276.431)\tETA 0:18:24\tLoss 0.5249 (0.5275)\n",
      "Epoch: [8][160/792]\tTime 1.745 (285.329)\tETA 0:18:23\tLoss 0.5295 (0.5275)\n",
      "Epoch: [8][165/792]\tTime 1.738 (294.028)\tETA 0:18:09\tLoss 0.5249 (0.5275)\n",
      "Epoch: [8][170/792]\tTime 1.710 (302.665)\tETA 0:17:43\tLoss 0.5250 (0.5275)\n",
      "Epoch: [8][175/792]\tTime 1.777 (311.509)\tETA 0:18:16\tLoss 0.5276 (0.5274)\n",
      "Epoch: [8][180/792]\tTime 1.789 (320.442)\tETA 0:18:14\tLoss 0.5241 (0.5274)\n",
      "Epoch: [8][185/792]\tTime 1.737 (329.798)\tETA 0:17:34\tLoss 0.5274 (0.5274)\n",
      "Epoch: [8][190/792]\tTime 1.804 (338.559)\tETA 0:18:05\tLoss 0.5279 (0.5274)\n",
      "Epoch: [8][195/792]\tTime 1.811 (347.538)\tETA 0:18:01\tLoss 0.5257 (0.5274)\n",
      "Epoch: [8][200/792]\tTime 1.784 (356.506)\tETA 0:17:36\tLoss 0.5272 (0.5274)\n",
      "Epoch: [8][205/792]\tTime 1.788 (365.598)\tETA 0:17:29\tLoss 0.5271 (0.5273)\n",
      "Epoch: [8][210/792]\tTime 1.840 (374.569)\tETA 0:17:51\tLoss 0.5259 (0.5273)\n",
      "Epoch: [8][215/792]\tTime 1.722 (383.412)\tETA 0:16:33\tLoss 0.5277 (0.5273)\n",
      "Epoch: [8][220/792]\tTime 1.793 (392.348)\tETA 0:17:05\tLoss 0.5235 (0.5273)\n",
      "Epoch: [8][225/792]\tTime 1.763 (401.292)\tETA 0:16:39\tLoss 0.5257 (0.5273)\n",
      "Epoch: [8][230/792]\tTime 1.805 (410.797)\tETA 0:16:54\tLoss 0.5275 (0.5273)\n",
      "Epoch: [8][235/792]\tTime 1.787 (419.747)\tETA 0:16:35\tLoss 0.5283 (0.5272)\n",
      "Epoch: [8][240/792]\tTime 1.712 (429.024)\tETA 0:15:45\tLoss 0.5334 (0.5273)\n",
      "Epoch: [8][245/792]\tTime 1.852 (438.202)\tETA 0:16:52\tLoss 0.5250 (0.5273)\n",
      "Epoch: [8][250/792]\tTime 1.723 (447.132)\tETA 0:15:34\tLoss 0.5257 (0.5273)\n",
      "Epoch: [8][255/792]\tTime 1.812 (455.972)\tETA 0:16:13\tLoss 0.5226 (0.5273)\n",
      "Epoch: [8][260/792]\tTime 1.776 (464.912)\tETA 0:15:44\tLoss 0.5271 (0.5273)\n",
      "Epoch: [8][265/792]\tTime 1.786 (473.878)\tETA 0:15:41\tLoss 0.5281 (0.5273)\n",
      "Epoch: [8][270/792]\tTime 1.828 (483.359)\tETA 0:15:53\tLoss 0.5258 (0.5273)\n",
      "Epoch: [8][275/792]\tTime 1.891 (492.469)\tETA 0:16:17\tLoss 0.5254 (0.5273)\n",
      "Epoch: [8][280/792]\tTime 1.754 (501.364)\tETA 0:14:58\tLoss 0.5258 (0.5272)\n",
      "Epoch: [8][285/792]\tTime 1.754 (510.178)\tETA 0:14:49\tLoss 0.5282 (0.5273)\n",
      "Epoch: [8][290/792]\tTime 1.762 (519.105)\tETA 0:14:44\tLoss 0.5273 (0.5272)\n",
      "Epoch: [8][295/792]\tTime 1.758 (528.060)\tETA 0:14:33\tLoss 0.5273 (0.5273)\n",
      "Epoch: [8][300/792]\tTime 1.825 (537.007)\tETA 0:14:58\tLoss 0.5253 (0.5273)\n",
      "Epoch: [8][305/792]\tTime 1.950 (546.234)\tETA 0:15:49\tLoss 0.5336 (0.5273)\n",
      "Epoch: [8][310/792]\tTime 1.799 (555.287)\tETA 0:14:27\tLoss 0.5263 (0.5273)\n",
      "Epoch: [8][315/792]\tTime 1.870 (564.452)\tETA 0:14:51\tLoss 0.5263 (0.5273)\n",
      "Epoch: [8][320/792]\tTime 1.812 (573.766)\tETA 0:14:15\tLoss 0.5272 (0.5273)\n",
      "Epoch: [8][325/792]\tTime 1.768 (582.623)\tETA 0:13:45\tLoss 0.5311 (0.5274)\n",
      "Epoch: [8][330/792]\tTime 1.786 (591.592)\tETA 0:13:45\tLoss 0.5283 (0.5274)\n",
      "Epoch: [8][335/792]\tTime 1.783 (600.529)\tETA 0:13:34\tLoss 0.5241 (0.5274)\n",
      "Epoch: [8][340/792]\tTime 1.814 (609.670)\tETA 0:13:40\tLoss 0.5298 (0.5274)\n",
      "Epoch: [8][345/792]\tTime 1.882 (618.941)\tETA 0:14:01\tLoss 0.5254 (0.5274)\n",
      "Epoch: [8][350/792]\tTime 1.830 (627.881)\tETA 0:13:28\tLoss 0.5244 (0.5273)\n",
      "Epoch: [8][355/792]\tTime 1.761 (636.919)\tETA 0:12:49\tLoss 0.5292 (0.5274)\n",
      "Epoch: [8][360/792]\tTime 1.759 (645.614)\tETA 0:12:39\tLoss 0.5280 (0.5274)\n",
      "Epoch: [8][365/792]\tTime 1.802 (654.517)\tETA 0:12:49\tLoss 0.5261 (0.5274)\n",
      "Epoch: [8][370/792]\tTime 1.837 (663.452)\tETA 0:12:55\tLoss 0.5276 (0.5274)\n",
      "Epoch: [8][375/792]\tTime 1.750 (672.396)\tETA 0:12:09\tLoss 0.5264 (0.5274)\n",
      "Epoch: [8][380/792]\tTime 1.777 (681.342)\tETA 0:12:11\tLoss 0.5277 (0.5273)\n",
      "Epoch: [8][385/792]\tTime 1.859 (690.392)\tETA 0:12:36\tLoss 0.5287 (0.5274)\n",
      "Epoch: [8][390/792]\tTime 1.744 (699.218)\tETA 0:11:41\tLoss 0.5265 (0.5274)\n",
      "Epoch: [8][395/792]\tTime 1.766 (708.131)\tETA 0:11:41\tLoss 0.5311 (0.5274)\n",
      "Epoch: [8][400/792]\tTime 1.763 (716.959)\tETA 0:11:31\tLoss 0.5285 (0.5274)\n",
      "Epoch: [8][405/792]\tTime 1.754 (725.687)\tETA 0:11:18\tLoss 0.5272 (0.5274)\n",
      "Epoch: [8][410/792]\tTime 1.828 (734.631)\tETA 0:11:38\tLoss 0.5262 (0.5273)\n",
      "Epoch: [8][415/792]\tTime 1.768 (743.508)\tETA 0:11:06\tLoss 0.5259 (0.5273)\n",
      "Epoch: [8][420/792]\tTime 1.797 (752.308)\tETA 0:11:08\tLoss 0.5265 (0.5273)\n",
      "Epoch: [8][425/792]\tTime 1.753 (761.196)\tETA 0:10:43\tLoss 0.5252 (0.5273)\n",
      "Epoch: [8][430/792]\tTime 1.752 (770.137)\tETA 0:10:34\tLoss 0.5254 (0.5273)\n",
      "Epoch: [8][435/792]\tTime 1.788 (779.038)\tETA 0:10:38\tLoss 0.5236 (0.5273)\n",
      "Epoch: [8][440/792]\tTime 1.723 (787.927)\tETA 0:10:06\tLoss 0.5260 (0.5273)\n",
      "Epoch: [8][445/792]\tTime 1.782 (796.787)\tETA 0:10:18\tLoss 0.5298 (0.5273)\n",
      "Epoch: [8][450/792]\tTime 2.216 (806.176)\tETA 0:12:38\tLoss 0.5247 (0.5273)\n",
      "Epoch: [8][455/792]\tTime 1.834 (815.196)\tETA 0:10:18\tLoss 0.5271 (0.5273)\n",
      "Epoch: [8][460/792]\tTime 1.755 (823.958)\tETA 0:09:42\tLoss 0.5270 (0.5273)\n",
      "Epoch: [8][465/792]\tTime 1.767 (832.916)\tETA 0:09:37\tLoss 0.5233 (0.5273)\n",
      "Epoch: [8][470/792]\tTime 1.827 (842.084)\tETA 0:09:48\tLoss 0.5288 (0.5273)\n",
      "Epoch: [8][475/792]\tTime 1.774 (851.067)\tETA 0:09:22\tLoss 0.5249 (0.5273)\n",
      "Epoch: [8][480/792]\tTime 1.840 (860.162)\tETA 0:09:34\tLoss 0.5251 (0.5272)\n",
      "Epoch: [8][485/792]\tTime 1.745 (869.230)\tETA 0:08:55\tLoss 0.5249 (0.5272)\n",
      "Epoch: [8][490/792]\tTime 1.738 (877.964)\tETA 0:08:44\tLoss 0.5293 (0.5272)\n",
      "Epoch: [8][495/792]\tTime 1.802 (886.916)\tETA 0:08:55\tLoss 0.5269 (0.5272)\n",
      "Epoch: [8][500/792]\tTime 1.757 (895.855)\tETA 0:08:33\tLoss 0.5273 (0.5272)\n",
      "Epoch: [8][505/792]\tTime 1.777 (904.717)\tETA 0:08:30\tLoss 0.5277 (0.5272)\n",
      "Epoch: [8][510/792]\tTime 1.764 (913.540)\tETA 0:08:17\tLoss 0.5288 (0.5272)\n",
      "Epoch: [8][515/792]\tTime 1.759 (922.496)\tETA 0:08:07\tLoss 0.5257 (0.5272)\n",
      "Epoch: [8][520/792]\tTime 1.871 (931.254)\tETA 0:08:28\tLoss 0.5282 (0.5272)\n",
      "Epoch: [8][525/792]\tTime 1.730 (940.211)\tETA 0:07:41\tLoss 0.5269 (0.5272)\n",
      "Epoch: [8][530/792]\tTime 1.869 (949.165)\tETA 0:08:09\tLoss 0.5233 (0.5272)\n",
      "Epoch: [8][535/792]\tTime 1.735 (958.097)\tETA 0:07:25\tLoss 0.5260 (0.5272)\n",
      "Epoch: [8][540/792]\tTime 1.773 (967.130)\tETA 0:07:26\tLoss 0.5281 (0.5272)\n",
      "Epoch: [8][545/792]\tTime 1.754 (976.032)\tETA 0:07:13\tLoss 0.5295 (0.5272)\n",
      "Epoch: [8][550/792]\tTime 1.773 (984.987)\tETA 0:07:09\tLoss 0.5225 (0.5272)\n",
      "Epoch: [8][555/792]\tTime 1.823 (994.063)\tETA 0:07:12\tLoss 0.5272 (0.5272)\n",
      "Epoch: [8][560/792]\tTime 1.716 (1002.889)\tETA 0:06:38\tLoss 0.5267 (0.5272)\n",
      "Epoch: [8][565/792]\tTime 1.760 (1011.818)\tETA 0:06:39\tLoss 0.5301 (0.5272)\n",
      "Epoch: [8][570/792]\tTime 1.739 (1020.967)\tETA 0:06:26\tLoss 0.5261 (0.5272)\n",
      "Epoch: [8][575/792]\tTime 1.835 (1030.067)\tETA 0:06:38\tLoss 0.5245 (0.5272)\n",
      "Epoch: [8][580/792]\tTime 1.799 (1039.028)\tETA 0:06:21\tLoss 0.5277 (0.5271)\n",
      "Epoch: [8][585/792]\tTime 1.881 (1048.055)\tETA 0:06:29\tLoss 0.5238 (0.5271)\n",
      "Epoch: [8][590/792]\tTime 1.792 (1057.077)\tETA 0:06:01\tLoss 0.5272 (0.5271)\n",
      "Epoch: [8][595/792]\tTime 1.758 (1065.943)\tETA 0:05:46\tLoss 0.5268 (0.5271)\n",
      "Epoch: [8][600/792]\tTime 1.849 (1074.937)\tETA 0:05:54\tLoss 0.5283 (0.5271)\n",
      "Epoch: [8][605/792]\tTime 1.843 (1083.883)\tETA 0:05:44\tLoss 0.5249 (0.5271)\n",
      "Epoch: [8][610/792]\tTime 1.757 (1092.931)\tETA 0:05:19\tLoss 0.5242 (0.5271)\n",
      "Epoch: [8][615/792]\tTime 1.812 (1101.842)\tETA 0:05:20\tLoss 0.5232 (0.5271)\n",
      "Epoch: [8][620/792]\tTime 1.860 (1110.934)\tETA 0:05:19\tLoss 0.5347 (0.5271)\n",
      "Epoch: [8][625/792]\tTime 1.760 (1119.836)\tETA 0:04:53\tLoss 0.5253 (0.5271)\n",
      "Epoch: [8][630/792]\tTime 1.965 (1128.988)\tETA 0:05:18\tLoss 0.5270 (0.5271)\n",
      "Epoch: [8][635/792]\tTime 1.782 (1137.862)\tETA 0:04:39\tLoss 0.5270 (0.5271)\n",
      "Epoch: [8][640/792]\tTime 1.809 (1146.823)\tETA 0:04:34\tLoss 0.5259 (0.5271)\n",
      "Epoch: [8][645/792]\tTime 1.774 (1155.661)\tETA 0:04:20\tLoss 0.5256 (0.5271)\n",
      "Epoch: [8][650/792]\tTime 1.784 (1164.651)\tETA 0:04:13\tLoss 0.5268 (0.5271)\n",
      "Epoch: [8][655/792]\tTime 1.794 (1173.569)\tETA 0:04:05\tLoss 0.5271 (0.5271)\n",
      "Epoch: [8][660/792]\tTime 1.782 (1182.431)\tETA 0:03:55\tLoss 0.5295 (0.5271)\n",
      "Epoch: [8][665/792]\tTime 1.800 (1191.492)\tETA 0:03:48\tLoss 0.5315 (0.5271)\n",
      "Epoch: [8][670/792]\tTime 1.818 (1200.499)\tETA 0:03:41\tLoss 0.5284 (0.5271)\n",
      "Epoch: [8][675/792]\tTime 1.730 (1209.431)\tETA 0:03:22\tLoss 0.5291 (0.5272)\n",
      "Epoch: [8][680/792]\tTime 1.762 (1218.207)\tETA 0:03:17\tLoss 0.5267 (0.5271)\n",
      "Epoch: [8][685/792]\tTime 1.755 (1226.977)\tETA 0:03:07\tLoss 0.5268 (0.5271)\n",
      "Epoch: [8][690/792]\tTime 1.819 (1235.849)\tETA 0:03:05\tLoss 0.5240 (0.5271)\n",
      "Epoch: [8][695/792]\tTime 1.798 (1244.812)\tETA 0:02:54\tLoss 0.5275 (0.5271)\n",
      "Epoch: [8][700/792]\tTime 1.795 (1253.714)\tETA 0:02:45\tLoss 0.5235 (0.5271)\n",
      "Epoch: [8][705/792]\tTime 1.740 (1262.639)\tETA 0:02:31\tLoss 0.5232 (0.5271)\n",
      "Epoch: [8][710/792]\tTime 1.777 (1271.607)\tETA 0:02:25\tLoss 0.5285 (0.5271)\n",
      "Epoch: [8][715/792]\tTime 1.797 (1280.496)\tETA 0:02:18\tLoss 0.5261 (0.5271)\n",
      "Epoch: [8][720/792]\tTime 1.833 (1289.409)\tETA 0:02:12\tLoss 0.5276 (0.5271)\n",
      "Epoch: [8][725/792]\tTime 1.782 (1298.206)\tETA 0:01:59\tLoss 0.5271 (0.5271)\n",
      "Epoch: [8][730/792]\tTime 1.819 (1307.227)\tETA 0:01:52\tLoss 0.5248 (0.5271)\n",
      "Epoch: [8][735/792]\tTime 1.793 (1316.259)\tETA 0:01:42\tLoss 0.5244 (0.5271)\n",
      "Epoch: [8][740/792]\tTime 1.810 (1325.338)\tETA 0:01:34\tLoss 0.5258 (0.5271)\n",
      "Epoch: [8][745/792]\tTime 1.881 (1334.385)\tETA 0:01:28\tLoss 0.5299 (0.5271)\n",
      "Epoch: [8][750/792]\tTime 1.777 (1343.343)\tETA 0:01:14\tLoss 0.5254 (0.5271)\n",
      "Epoch: [8][755/792]\tTime 1.790 (1352.446)\tETA 0:01:06\tLoss 0.5248 (0.5271)\n",
      "Epoch: [8][760/792]\tTime 1.747 (1361.353)\tETA 0:00:55\tLoss 0.5251 (0.5271)\n",
      "Epoch: [8][765/792]\tTime 1.735 (1370.114)\tETA 0:00:46\tLoss 0.5283 (0.5271)\n",
      "Epoch: [8][770/792]\tTime 1.777 (1379.061)\tETA 0:00:39\tLoss 0.5255 (0.5271)\n",
      "Epoch: [8][775/792]\tTime 1.768 (1387.824)\tETA 0:00:30\tLoss 0.5235 (0.5271)\n",
      "Epoch: [8][780/792]\tTime 1.776 (1396.746)\tETA 0:00:21\tLoss 0.5277 (0.5271)\n",
      "Epoch: [8][785/792]\tTime 1.810 (1405.477)\tETA 0:00:12\tLoss 0.5258 (0.5271)\n",
      "Epoch: [8][790/792]\tTime 1.730 (1414.210)\tETA 0:00:03\tLoss 0.5266 (0.5271)\n",
      "Epoch: [9][0/792]\tTime 1.743 (1.743)\tETA 0:23:00\tLoss 0.5236 (0.5236)\n",
      "Epoch: [9][5/792]\tTime 1.731 (10.557)\tETA 0:22:42\tLoss 0.5253 (0.5247)\n",
      "Epoch: [9][10/792]\tTime 1.837 (19.532)\tETA 0:23:56\tLoss 0.5263 (0.5250)\n",
      "Epoch: [9][15/792]\tTime 1.748 (28.466)\tETA 0:22:38\tLoss 0.5275 (0.5259)\n",
      "Epoch: [9][20/792]\tTime 1.710 (37.166)\tETA 0:22:00\tLoss 0.5336 (0.5265)\n",
      "Epoch: [9][25/792]\tTime 1.771 (45.957)\tETA 0:22:38\tLoss 0.5266 (0.5266)\n",
      "Epoch: [9][30/792]\tTime 1.853 (54.948)\tETA 0:23:31\tLoss 0.5316 (0.5271)\n",
      "Epoch: [9][35/792]\tTime 1.746 (64.050)\tETA 0:22:01\tLoss 0.5249 (0.5274)\n",
      "Epoch: [9][40/792]\tTime 1.823 (72.827)\tETA 0:22:50\tLoss 0.5310 (0.5276)\n",
      "Epoch: [9][45/792]\tTime 1.811 (81.840)\tETA 0:22:32\tLoss 0.5313 (0.5278)\n",
      "Epoch: [9][50/792]\tTime 1.884 (91.078)\tETA 0:23:17\tLoss 0.5322 (0.5282)\n",
      "Epoch: [9][55/792]\tTime 1.799 (100.209)\tETA 0:22:05\tLoss 0.5243 (0.5284)\n",
      "Epoch: [9][60/792]\tTime 1.738 (109.111)\tETA 0:21:12\tLoss 0.5311 (0.5285)\n",
      "Epoch: [9][65/792]\tTime 1.991 (118.470)\tETA 0:24:07\tLoss 0.5324 (0.5285)\n",
      "Epoch: [9][70/792]\tTime 2.237 (128.126)\tETA 0:26:55\tLoss 0.5277 (0.5285)\n",
      "Epoch: [9][75/792]\tTime 1.882 (137.659)\tETA 0:22:29\tLoss 0.5233 (0.5284)\n",
      "Epoch: [9][80/792]\tTime 1.874 (146.728)\tETA 0:22:14\tLoss 0.5317 (0.5284)\n",
      "Epoch: [9][85/792]\tTime 1.825 (155.962)\tETA 0:21:30\tLoss 0.5317 (0.5285)\n",
      "Epoch: [9][90/792]\tTime 1.703 (164.733)\tETA 0:19:55\tLoss 0.5303 (0.5286)\n",
      "Epoch: [9][95/792]\tTime 1.752 (173.539)\tETA 0:20:21\tLoss 0.5251 (0.5286)\n",
      "Epoch: [9][100/792]\tTime 1.828 (182.407)\tETA 0:21:04\tLoss 0.5303 (0.5287)\n",
      "Epoch: [9][105/792]\tTime 1.778 (191.344)\tETA 0:20:21\tLoss 0.5378 (0.5288)\n",
      "Epoch: [9][110/792]\tTime 1.781 (200.442)\tETA 0:20:14\tLoss 0.5317 (0.5290)\n",
      "Epoch: [9][115/792]\tTime 1.823 (209.352)\tETA 0:20:34\tLoss 0.5272 (0.5291)\n",
      "Epoch: [9][120/792]\tTime 1.794 (218.119)\tETA 0:20:05\tLoss 0.5251 (0.5292)\n",
      "Epoch: [9][125/792]\tTime 1.785 (227.026)\tETA 0:19:50\tLoss 0.5303 (0.5292)\n",
      "Epoch: [9][130/792]\tTime 1.859 (235.970)\tETA 0:20:30\tLoss 0.5319 (0.5293)\n",
      "Epoch: [9][135/792]\tTime 1.756 (244.712)\tETA 0:19:13\tLoss 0.5367 (0.5294)\n",
      "Epoch: [9][140/792]\tTime 1.791 (253.625)\tETA 0:19:27\tLoss 0.5276 (0.5294)\n",
      "Epoch: [9][145/792]\tTime 1.776 (262.577)\tETA 0:19:09\tLoss 0.5249 (0.5293)\n",
      "Epoch: [9][150/792]\tTime 1.727 (271.466)\tETA 0:18:28\tLoss 0.5268 (0.5291)\n",
      "Epoch: [9][155/792]\tTime 1.959 (280.615)\tETA 0:20:47\tLoss 0.5249 (0.5290)\n",
      "Epoch: [9][160/792]\tTime 1.809 (289.660)\tETA 0:19:03\tLoss 0.5260 (0.5290)\n",
      "Epoch: [9][165/792]\tTime 1.803 (298.567)\tETA 0:18:50\tLoss 0.5240 (0.5289)\n",
      "Epoch: [9][170/792]\tTime 1.801 (307.282)\tETA 0:18:40\tLoss 0.5264 (0.5288)\n",
      "Epoch: [9][175/792]\tTime 1.744 (316.127)\tETA 0:17:55\tLoss 0.5249 (0.5287)\n",
      "Epoch: [9][180/792]\tTime 1.748 (325.218)\tETA 0:17:49\tLoss 0.5236 (0.5286)\n",
      "Epoch: [9][185/792]\tTime 1.810 (334.206)\tETA 0:18:18\tLoss 0.5232 (0.5285)\n",
      "Epoch: [9][190/792]\tTime 1.886 (343.354)\tETA 0:18:55\tLoss 0.5232 (0.5284)\n",
      "Epoch: [9][195/792]\tTime 1.804 (352.538)\tETA 0:17:56\tLoss 0.5257 (0.5284)\n",
      "Epoch: [9][200/792]\tTime 1.809 (361.435)\tETA 0:17:51\tLoss 0.5267 (0.5283)\n",
      "Epoch: [9][205/792]\tTime 1.768 (370.882)\tETA 0:17:17\tLoss 0.5265 (0.5283)\n",
      "Epoch: [9][210/792]\tTime 1.794 (380.000)\tETA 0:17:23\tLoss 0.5229 (0.5282)\n",
      "Epoch: [9][215/792]\tTime 1.765 (388.995)\tETA 0:16:58\tLoss 0.5269 (0.5282)\n",
      "Epoch: [9][220/792]\tTime 1.772 (398.089)\tETA 0:16:53\tLoss 0.5323 (0.5281)\n",
      "Epoch: [9][225/792]\tTime 1.778 (406.904)\tETA 0:16:47\tLoss 0.5230 (0.5281)\n",
      "Epoch: [9][230/792]\tTime 1.816 (415.757)\tETA 0:17:00\tLoss 0.5272 (0.5280)\n",
      "Epoch: [9][235/792]\tTime 1.751 (424.691)\tETA 0:16:15\tLoss 0.5270 (0.5280)\n",
      "Epoch: [9][240/792]\tTime 1.806 (433.544)\tETA 0:16:36\tLoss 0.5265 (0.5279)\n",
      "Epoch: [9][245/792]\tTime 1.776 (442.522)\tETA 0:16:11\tLoss 0.5235 (0.5279)\n",
      "Epoch: [9][250/792]\tTime 1.811 (451.207)\tETA 0:16:21\tLoss 0.5241 (0.5278)\n",
      "Epoch: [9][255/792]\tTime 1.756 (459.902)\tETA 0:15:43\tLoss 0.5255 (0.5278)\n",
      "Epoch: [9][260/792]\tTime 1.757 (468.691)\tETA 0:15:34\tLoss 0.5290 (0.5277)\n",
      "Epoch: [9][265/792]\tTime 1.812 (477.510)\tETA 0:15:54\tLoss 0.5301 (0.5277)\n",
      "Epoch: [9][270/792]\tTime 1.747 (486.381)\tETA 0:15:11\tLoss 0.5277 (0.5277)\n",
      "Epoch: [9][275/792]\tTime 1.852 (495.230)\tETA 0:15:57\tLoss 0.5276 (0.5277)\n",
      "Epoch: [9][280/792]\tTime 1.882 (504.604)\tETA 0:16:03\tLoss 0.5240 (0.5277)\n",
      "Epoch: [9][285/792]\tTime 1.757 (513.414)\tETA 0:14:50\tLoss 0.5248 (0.5276)\n",
      "Epoch: [9][290/792]\tTime 1.808 (522.407)\tETA 0:15:07\tLoss 0.5268 (0.5276)\n",
      "Epoch: [9][295/792]\tTime 1.777 (531.543)\tETA 0:14:42\tLoss 0.5283 (0.5276)\n",
      "Epoch: [9][300/792]\tTime 1.815 (540.492)\tETA 0:14:52\tLoss 0.5271 (0.5276)\n",
      "Epoch: [9][305/792]\tTime 1.756 (549.205)\tETA 0:14:15\tLoss 0.5244 (0.5275)\n",
      "Epoch: [9][310/792]\tTime 1.824 (558.180)\tETA 0:14:39\tLoss 0.5268 (0.5275)\n",
      "Epoch: [9][315/792]\tTime 1.961 (567.439)\tETA 0:15:35\tLoss 0.5280 (0.5275)\n",
      "Epoch: [9][320/792]\tTime 1.833 (577.055)\tETA 0:14:25\tLoss 0.5269 (0.5274)\n",
      "Epoch: [9][325/792]\tTime 1.769 (586.151)\tETA 0:13:45\tLoss 0.5248 (0.5274)\n",
      "Epoch: [9][330/792]\tTime 1.779 (595.085)\tETA 0:13:42\tLoss 0.5255 (0.5274)\n",
      "Epoch: [9][335/792]\tTime 1.867 (604.137)\tETA 0:14:12\tLoss 0.5263 (0.5274)\n",
      "Epoch: [9][340/792]\tTime 1.743 (612.779)\tETA 0:13:07\tLoss 0.5285 (0.5274)\n",
      "Epoch: [9][345/792]\tTime 1.804 (621.832)\tETA 0:13:26\tLoss 0.5254 (0.5274)\n",
      "Epoch: [9][350/792]\tTime 1.808 (630.884)\tETA 0:13:19\tLoss 0.5289 (0.5274)\n",
      "Epoch: [9][355/792]\tTime 1.746 (639.720)\tETA 0:12:42\tLoss 0.5251 (0.5274)\n",
      "Epoch: [9][360/792]\tTime 1.801 (648.743)\tETA 0:12:57\tLoss 0.5256 (0.5273)\n",
      "Epoch: [9][365/792]\tTime 1.762 (657.521)\tETA 0:12:32\tLoss 0.5278 (0.5273)\n",
      "Epoch: [9][370/792]\tTime 1.767 (666.539)\tETA 0:12:25\tLoss 0.5260 (0.5273)\n",
      "Epoch: [9][375/792]\tTime 1.771 (675.481)\tETA 0:12:18\tLoss 0.5272 (0.5273)\n",
      "Epoch: [9][380/792]\tTime 1.771 (684.739)\tETA 0:12:09\tLoss 0.5237 (0.5272)\n",
      "Epoch: [9][385/792]\tTime 1.776 (694.113)\tETA 0:12:02\tLoss 0.5259 (0.5272)\n",
      "Epoch: [9][390/792]\tTime 1.818 (702.939)\tETA 0:12:10\tLoss 0.5262 (0.5272)\n",
      "Epoch: [9][395/792]\tTime 1.811 (711.997)\tETA 0:11:58\tLoss 0.5249 (0.5272)\n",
      "Epoch: [9][400/792]\tTime 1.803 (721.150)\tETA 0:11:46\tLoss 0.5288 (0.5272)\n",
      "Epoch: [9][405/792]\tTime 1.753 (730.098)\tETA 0:11:18\tLoss 0.5233 (0.5271)\n",
      "Epoch: [9][410/792]\tTime 1.773 (739.011)\tETA 0:11:17\tLoss 0.5254 (0.5271)\n",
      "Epoch: [9][415/792]\tTime 1.727 (748.129)\tETA 0:10:51\tLoss 0.5247 (0.5271)\n",
      "Epoch: [9][420/792]\tTime 1.834 (757.035)\tETA 0:11:22\tLoss 0.5250 (0.5271)\n",
      "Epoch: [9][425/792]\tTime 1.823 (765.898)\tETA 0:11:09\tLoss 0.5258 (0.5271)\n",
      "Epoch: [9][430/792]\tTime 1.841 (774.937)\tETA 0:11:06\tLoss 0.5290 (0.5271)\n",
      "Epoch: [9][435/792]\tTime 1.795 (783.748)\tETA 0:10:40\tLoss 0.5291 (0.5271)\n",
      "Epoch: [9][440/792]\tTime 1.787 (792.711)\tETA 0:10:29\tLoss 0.5238 (0.5271)\n",
      "Epoch: [9][445/792]\tTime 1.810 (801.680)\tETA 0:10:28\tLoss 0.5231 (0.5270)\n",
      "Epoch: [9][450/792]\tTime 1.784 (810.420)\tETA 0:10:10\tLoss 0.5259 (0.5270)\n",
      "Epoch: [9][455/792]\tTime 1.804 (819.369)\tETA 0:10:08\tLoss 0.5222 (0.5270)\n",
      "Epoch: [9][460/792]\tTime 1.754 (828.240)\tETA 0:09:42\tLoss 0.5239 (0.5270)\n",
      "Epoch: [9][465/792]\tTime 1.788 (837.211)\tETA 0:09:44\tLoss 0.5224 (0.5269)\n",
      "Epoch: [9][470/792]\tTime 1.789 (846.076)\tETA 0:09:35\tLoss 0.5238 (0.5269)\n",
      "Epoch: [9][475/792]\tTime 1.699 (854.828)\tETA 0:08:58\tLoss 0.5244 (0.5269)\n",
      "Epoch: [9][480/792]\tTime 1.709 (863.664)\tETA 0:08:53\tLoss 0.5279 (0.5269)\n",
      "Epoch: [9][485/792]\tTime 1.734 (872.467)\tETA 0:08:52\tLoss 0.5241 (0.5269)\n",
      "Epoch: [9][490/792]\tTime 1.738 (881.283)\tETA 0:08:44\tLoss 0.5306 (0.5269)\n",
      "Epoch: [9][495/792]\tTime 1.743 (890.159)\tETA 0:08:37\tLoss 0.5266 (0.5269)\n",
      "Epoch: [9][500/792]\tTime 1.812 (899.089)\tETA 0:08:49\tLoss 0.5249 (0.5269)\n",
      "Epoch: [9][505/792]\tTime 1.792 (908.005)\tETA 0:08:34\tLoss 0.5282 (0.5270)\n",
      "Epoch: [9][510/792]\tTime 1.694 (916.728)\tETA 0:07:57\tLoss 0.5308 (0.5270)\n",
      "Epoch: [9][515/792]\tTime 1.791 (926.059)\tETA 0:08:16\tLoss 0.5269 (0.5270)\n",
      "Epoch: [9][520/792]\tTime 1.802 (935.021)\tETA 0:08:10\tLoss 0.5265 (0.5270)\n",
      "Epoch: [9][525/792]\tTime 1.842 (943.861)\tETA 0:08:11\tLoss 0.5286 (0.5270)\n",
      "Epoch: [9][530/792]\tTime 1.785 (952.829)\tETA 0:07:47\tLoss 0.5270 (0.5269)\n",
      "Epoch: [9][535/792]\tTime 1.785 (961.987)\tETA 0:07:38\tLoss 0.5250 (0.5269)\n",
      "Epoch: [9][540/792]\tTime 1.813 (971.032)\tETA 0:07:36\tLoss 0.5260 (0.5269)\n",
      "Epoch: [9][545/792]\tTime 1.842 (979.966)\tETA 0:07:35\tLoss 0.5244 (0.5269)\n",
      "Epoch: [9][550/792]\tTime 1.764 (988.871)\tETA 0:07:06\tLoss 0.5321 (0.5269)\n",
      "Epoch: [9][555/792]\tTime 1.791 (998.021)\tETA 0:07:04\tLoss 0.5233 (0.5269)\n",
      "Epoch: [9][560/792]\tTime 1.783 (1007.001)\tETA 0:06:53\tLoss 0.5279 (0.5269)\n",
      "Epoch: [9][565/792]\tTime 1.771 (1015.850)\tETA 0:06:41\tLoss 0.5246 (0.5269)\n",
      "Epoch: [9][570/792]\tTime 1.773 (1024.713)\tETA 0:06:33\tLoss 0.5294 (0.5269)\n",
      "Epoch: [9][575/792]\tTime 1.821 (1033.633)\tETA 0:06:35\tLoss 0.5281 (0.5269)\n",
      "Epoch: [9][580/792]\tTime 1.754 (1042.791)\tETA 0:06:11\tLoss 0.5277 (0.5269)\n",
      "Epoch: [9][585/792]\tTime 1.735 (1051.684)\tETA 0:05:59\tLoss 0.5282 (0.5269)\n",
      "Epoch: [9][590/792]\tTime 1.726 (1060.541)\tETA 0:05:48\tLoss 0.5285 (0.5269)\n",
      "Epoch: [9][595/792]\tTime 1.773 (1069.643)\tETA 0:05:49\tLoss 0.5249 (0.5269)\n",
      "Epoch: [9][600/792]\tTime 1.802 (1078.731)\tETA 0:05:45\tLoss 0.5255 (0.5269)\n",
      "Epoch: [9][605/792]\tTime 1.802 (1087.761)\tETA 0:05:37\tLoss 0.5250 (0.5269)\n",
      "Epoch: [9][610/792]\tTime 1.832 (1096.714)\tETA 0:05:33\tLoss 0.5268 (0.5269)\n",
      "Epoch: [9][615/792]\tTime 1.875 (1106.379)\tETA 0:05:31\tLoss 0.5242 (0.5269)\n",
      "Epoch: [9][620/792]\tTime 1.812 (1115.370)\tETA 0:05:11\tLoss 0.5262 (0.5268)\n",
      "Epoch: [9][625/792]\tTime 1.690 (1124.040)\tETA 0:04:42\tLoss 0.5255 (0.5268)\n",
      "Epoch: [9][630/792]\tTime 1.799 (1132.913)\tETA 0:04:51\tLoss 0.5249 (0.5268)\n",
      "Epoch: [9][635/792]\tTime 1.761 (1141.762)\tETA 0:04:36\tLoss 0.5278 (0.5268)\n",
      "Epoch: [9][640/792]\tTime 1.793 (1150.788)\tETA 0:04:32\tLoss 0.5284 (0.5268)\n",
      "Epoch: [9][645/792]\tTime 1.845 (1159.989)\tETA 0:04:31\tLoss 0.5284 (0.5268)\n",
      "Epoch: [9][650/792]\tTime 1.828 (1169.153)\tETA 0:04:19\tLoss 0.5265 (0.5268)\n",
      "Epoch: [9][655/792]\tTime 1.750 (1177.985)\tETA 0:03:59\tLoss 0.5288 (0.5268)\n",
      "Epoch: [9][660/792]\tTime 1.793 (1186.787)\tETA 0:03:56\tLoss 0.5245 (0.5268)\n",
      "Epoch: [9][665/792]\tTime 1.776 (1195.593)\tETA 0:03:45\tLoss 0.5262 (0.5268)\n",
      "Epoch: [9][670/792]\tTime 1.814 (1204.649)\tETA 0:03:41\tLoss 0.5266 (0.5268)\n",
      "Epoch: [9][675/792]\tTime 1.770 (1213.404)\tETA 0:03:27\tLoss 0.5268 (0.5268)\n",
      "Epoch: [9][680/792]\tTime 1.790 (1222.339)\tETA 0:03:20\tLoss 0.5253 (0.5268)\n",
      "Epoch: [9][685/792]\tTime 1.834 (1231.389)\tETA 0:03:16\tLoss 0.5255 (0.5268)\n",
      "Epoch: [9][690/792]\tTime 1.866 (1240.532)\tETA 0:03:10\tLoss 0.5241 (0.5268)\n",
      "Epoch: [9][695/792]\tTime 1.838 (1249.496)\tETA 0:02:58\tLoss 0.5257 (0.5267)\n",
      "Epoch: [9][700/792]\tTime 1.783 (1258.390)\tETA 0:02:44\tLoss 0.5279 (0.5267)\n",
      "Epoch: [9][705/792]\tTime 1.807 (1267.266)\tETA 0:02:37\tLoss 0.5252 (0.5267)\n",
      "Epoch: [9][710/792]\tTime 1.738 (1276.074)\tETA 0:02:22\tLoss 0.5243 (0.5267)\n",
      "Epoch: [9][715/792]\tTime 1.786 (1284.974)\tETA 0:02:17\tLoss 0.5262 (0.5267)\n",
      "Epoch: [9][720/792]\tTime 1.727 (1293.805)\tETA 0:02:04\tLoss 0.5258 (0.5267)\n",
      "Epoch: [9][725/792]\tTime 1.769 (1302.582)\tETA 0:01:58\tLoss 0.5278 (0.5267)\n",
      "Epoch: [9][730/792]\tTime 1.748 (1311.425)\tETA 0:01:48\tLoss 0.5248 (0.5267)\n",
      "Epoch: [9][735/792]\tTime 1.801 (1320.254)\tETA 0:01:42\tLoss 0.5239 (0.5267)\n",
      "Epoch: [9][740/792]\tTime 1.769 (1329.112)\tETA 0:01:31\tLoss 0.5274 (0.5267)\n",
      "Epoch: [9][745/792]\tTime 1.743 (1337.912)\tETA 0:01:21\tLoss 0.5259 (0.5266)\n",
      "Epoch: [9][750/792]\tTime 1.793 (1346.921)\tETA 0:01:15\tLoss 0.5270 (0.5266)\n",
      "Epoch: [9][755/792]\tTime 1.752 (1355.847)\tETA 0:01:04\tLoss 0.5261 (0.5266)\n",
      "Epoch: [9][760/792]\tTime 1.755 (1364.650)\tETA 0:00:56\tLoss 0.5249 (0.5266)\n",
      "Epoch: [9][765/792]\tTime 1.773 (1373.530)\tETA 0:00:47\tLoss 0.5252 (0.5266)\n",
      "Epoch: [9][770/792]\tTime 1.806 (1382.425)\tETA 0:00:39\tLoss 0.5239 (0.5266)\n",
      "Epoch: [9][775/792]\tTime 1.832 (1391.399)\tETA 0:00:31\tLoss 0.5262 (0.5266)\n",
      "Epoch: [9][780/792]\tTime 1.721 (1400.080)\tETA 0:00:20\tLoss 0.5248 (0.5266)\n",
      "Epoch: [9][785/792]\tTime 1.804 (1409.055)\tETA 0:00:12\tLoss 0.5220 (0.5266)\n",
      "Epoch: [9][790/792]\tTime 1.788 (1417.763)\tETA 0:00:03\tLoss 0.5258 (0.5266)\n",
      "Epoch: [10][0/792]\tTime 1.732 (1.732)\tETA 0:22:51\tLoss 0.5259 (0.5259)\n",
      "Epoch: [10][5/792]\tTime 1.805 (10.609)\tETA 0:23:40\tLoss 0.5270 (0.5267)\n",
      "Epoch: [10][10/792]\tTime 1.851 (19.601)\tETA 0:24:07\tLoss 0.5246 (0.5257)\n",
      "Epoch: [10][15/792]\tTime 1.723 (28.450)\tETA 0:22:18\tLoss 0.5279 (0.5261)\n",
      "Epoch: [10][20/792]\tTime 1.756 (37.212)\tETA 0:22:35\tLoss 0.5245 (0.5266)\n",
      "Epoch: [10][25/792]\tTime 1.818 (46.120)\tETA 0:23:14\tLoss 0.5262 (0.5268)\n",
      "Epoch: [10][30/792]\tTime 1.757 (55.198)\tETA 0:22:18\tLoss 0.5272 (0.5273)\n",
      "Epoch: [10][35/792]\tTime 1.798 (64.185)\tETA 0:22:40\tLoss 0.5296 (0.5277)\n",
      "Epoch: [10][40/792]\tTime 1.730 (72.934)\tETA 0:21:40\tLoss 0.5301 (0.5278)\n",
      "Epoch: [10][45/792]\tTime 1.788 (81.739)\tETA 0:22:15\tLoss 0.5262 (0.5276)\n",
      "Epoch: [10][50/792]\tTime 1.789 (90.543)\tETA 0:22:07\tLoss 0.5248 (0.5275)\n",
      "Epoch: [10][55/792]\tTime 1.751 (99.391)\tETA 0:21:30\tLoss 0.5325 (0.5273)\n",
      "Epoch: [10][60/792]\tTime 1.738 (108.113)\tETA 0:21:12\tLoss 0.5272 (0.5272)\n",
      "Epoch: [10][65/792]\tTime 1.765 (116.955)\tETA 0:21:22\tLoss 0.5241 (0.5272)\n",
      "Epoch: [10][70/792]\tTime 1.759 (125.730)\tETA 0:21:09\tLoss 0.5250 (0.5270)\n",
      "Epoch: [10][75/792]\tTime 1.829 (134.716)\tETA 0:21:51\tLoss 0.5251 (0.5269)\n",
      "Epoch: [10][80/792]\tTime 1.708 (143.457)\tETA 0:20:16\tLoss 0.5220 (0.5266)\n",
      "Epoch: [10][85/792]\tTime 1.794 (152.427)\tETA 0:21:08\tLoss 0.5269 (0.5265)\n",
      "Epoch: [10][90/792]\tTime 1.805 (161.508)\tETA 0:21:07\tLoss 0.5261 (0.5264)\n",
      "Epoch: [10][95/792]\tTime 1.855 (170.679)\tETA 0:21:32\tLoss 0.5248 (0.5263)\n",
      "Epoch: [10][100/792]\tTime 1.790 (179.542)\tETA 0:20:38\tLoss 0.5267 (0.5262)\n",
      "Epoch: [10][105/792]\tTime 1.767 (188.404)\tETA 0:20:14\tLoss 0.5215 (0.5261)\n",
      "Epoch: [10][110/792]\tTime 1.776 (197.361)\tETA 0:20:10\tLoss 0.5245 (0.5261)\n",
      "Epoch: [10][115/792]\tTime 1.693 (206.051)\tETA 0:19:06\tLoss 0.5250 (0.5261)\n",
      "Epoch: [10][120/792]\tTime 1.751 (214.850)\tETA 0:19:36\tLoss 0.5230 (0.5260)\n",
      "Epoch: [10][125/792]\tTime 1.742 (223.670)\tETA 0:19:21\tLoss 0.5244 (0.5259)\n",
      "Epoch: [10][130/792]\tTime 1.786 (232.415)\tETA 0:19:42\tLoss 0.5261 (0.5260)\n",
      "Epoch: [10][135/792]\tTime 1.742 (241.257)\tETA 0:19:04\tLoss 0.5230 (0.5259)\n",
      "Epoch: [10][140/792]\tTime 1.740 (250.022)\tETA 0:18:54\tLoss 0.5318 (0.5259)\n",
      "Epoch: [10][145/792]\tTime 1.755 (259.240)\tETA 0:18:55\tLoss 0.5229 (0.5259)\n",
      "Epoch: [10][150/792]\tTime 1.765 (267.990)\tETA 0:18:53\tLoss 0.5252 (0.5259)\n",
      "Epoch: [10][155/792]\tTime 1.739 (276.898)\tETA 0:18:28\tLoss 0.5297 (0.5259)\n",
      "Epoch: [10][160/792]\tTime 1.835 (286.112)\tETA 0:19:19\tLoss 0.5261 (0.5259)\n",
      "Epoch: [10][165/792]\tTime 1.810 (295.274)\tETA 0:18:55\tLoss 0.5262 (0.5259)\n",
      "Epoch: [10][170/792]\tTime 1.820 (304.386)\tETA 0:18:52\tLoss 0.5268 (0.5259)\n",
      "Epoch: [10][175/792]\tTime 1.750 (313.268)\tETA 0:17:59\tLoss 0.5246 (0.5259)\n",
      "Epoch: [10][180/792]\tTime 1.752 (322.188)\tETA 0:17:52\tLoss 0.5244 (0.5258)\n",
      "Epoch: [10][185/792]\tTime 1.726 (331.215)\tETA 0:17:27\tLoss 0.5258 (0.5258)\n",
      "Epoch: [10][190/792]\tTime 1.866 (340.594)\tETA 0:18:43\tLoss 0.5223 (0.5257)\n",
      "Epoch: [10][195/792]\tTime 1.705 (349.808)\tETA 0:16:57\tLoss 0.5245 (0.5257)\n",
      "Epoch: [10][200/792]\tTime 1.801 (358.820)\tETA 0:17:46\tLoss 0.5242 (0.5257)\n",
      "Epoch: [10][205/792]\tTime 1.775 (368.015)\tETA 0:17:21\tLoss 0.5305 (0.5257)\n",
      "Epoch: [10][210/792]\tTime 1.891 (377.155)\tETA 0:18:20\tLoss 0.5257 (0.5258)\n",
      "Epoch: [10][215/792]\tTime 1.800 (386.140)\tETA 0:17:18\tLoss 0.5258 (0.5258)\n",
      "Epoch: [10][220/792]\tTime 1.765 (395.187)\tETA 0:16:49\tLoss 0.5282 (0.5258)\n",
      "Epoch: [10][225/792]\tTime 1.766 (404.043)\tETA 0:16:41\tLoss 0.5254 (0.5259)\n",
      "Epoch: [10][230/792]\tTime 1.771 (413.091)\tETA 0:16:35\tLoss 0.5231 (0.5258)\n",
      "Epoch: [10][235/792]\tTime 1.769 (421.994)\tETA 0:16:25\tLoss 0.5263 (0.5258)\n",
      "Epoch: [10][240/792]\tTime 1.759 (430.858)\tETA 0:16:11\tLoss 0.5249 (0.5258)\n",
      "Epoch: [10][245/792]\tTime 1.802 (439.758)\tETA 0:16:25\tLoss 0.5259 (0.5258)\n",
      "Epoch: [10][250/792]\tTime 1.821 (448.705)\tETA 0:16:27\tLoss 0.5242 (0.5258)\n",
      "Epoch: [10][255/792]\tTime 1.747 (457.497)\tETA 0:15:37\tLoss 0.5242 (0.5258)\n",
      "Epoch: [10][260/792]\tTime 1.815 (466.468)\tETA 0:16:05\tLoss 0.5244 (0.5258)\n",
      "Epoch: [10][265/792]\tTime 1.771 (475.287)\tETA 0:15:33\tLoss 0.5238 (0.5258)\n",
      "Epoch: [10][270/792]\tTime 1.802 (484.229)\tETA 0:15:40\tLoss 0.5239 (0.5258)\n",
      "Epoch: [10][275/792]\tTime 1.726 (493.089)\tETA 0:14:52\tLoss 0.5240 (0.5257)\n",
      "Epoch: [10][280/792]\tTime 1.743 (501.862)\tETA 0:14:52\tLoss 0.5242 (0.5257)\n",
      "Epoch: [10][285/792]\tTime 1.798 (510.877)\tETA 0:15:11\tLoss 0.5268 (0.5257)\n",
      "Epoch: [10][290/792]\tTime 1.778 (519.785)\tETA 0:14:52\tLoss 0.5281 (0.5257)\n",
      "Epoch: [10][295/792]\tTime 1.833 (528.829)\tETA 0:15:10\tLoss 0.5237 (0.5257)\n",
      "Epoch: [10][300/792]\tTime 1.789 (537.735)\tETA 0:14:39\tLoss 0.5269 (0.5257)\n",
      "Epoch: [10][305/792]\tTime 1.779 (546.801)\tETA 0:14:26\tLoss 0.5270 (0.5257)\n",
      "Epoch: [10][310/792]\tTime 1.716 (555.686)\tETA 0:13:47\tLoss 0.5229 (0.5257)\n",
      "Epoch: [10][315/792]\tTime 1.742 (564.485)\tETA 0:13:51\tLoss 0.5287 (0.5257)\n",
      "Epoch: [10][320/792]\tTime 1.778 (573.395)\tETA 0:13:59\tLoss 0.5239 (0.5257)\n",
      "Epoch: [10][325/792]\tTime 1.853 (582.546)\tETA 0:14:25\tLoss 0.5248 (0.5257)\n",
      "Epoch: [10][330/792]\tTime 1.736 (591.343)\tETA 0:13:21\tLoss 0.5244 (0.5257)\n",
      "Epoch: [10][335/792]\tTime 1.746 (600.136)\tETA 0:13:18\tLoss 0.5242 (0.5257)\n",
      "Epoch: [10][340/792]\tTime 1.746 (608.967)\tETA 0:13:09\tLoss 0.5262 (0.5257)\n",
      "Epoch: [10][345/792]\tTime 1.820 (617.837)\tETA 0:13:33\tLoss 0.5278 (0.5257)\n",
      "Epoch: [10][350/792]\tTime 1.759 (626.909)\tETA 0:12:57\tLoss 0.5246 (0.5257)\n",
      "Epoch: [10][355/792]\tTime 1.754 (636.014)\tETA 0:12:46\tLoss 0.5239 (0.5257)\n",
      "Epoch: [10][360/792]\tTime 1.766 (645.076)\tETA 0:12:42\tLoss 0.5228 (0.5257)\n",
      "Epoch: [10][365/792]\tTime 1.790 (654.046)\tETA 0:12:44\tLoss 0.5209 (0.5257)\n",
      "Epoch: [10][370/792]\tTime 1.797 (662.994)\tETA 0:12:38\tLoss 0.5288 (0.5257)\n",
      "Epoch: [10][375/792]\tTime 1.839 (671.981)\tETA 0:12:46\tLoss 0.5235 (0.5257)\n",
      "Epoch: [10][380/792]\tTime 1.834 (681.086)\tETA 0:12:35\tLoss 0.5248 (0.5257)\n",
      "Epoch: [10][385/792]\tTime 1.811 (690.115)\tETA 0:12:17\tLoss 0.5237 (0.5257)\n",
      "Epoch: [10][390/792]\tTime 1.749 (698.852)\tETA 0:11:43\tLoss 0.5258 (0.5257)\n",
      "Epoch: [10][395/792]\tTime 1.791 (707.760)\tETA 0:11:50\tLoss 0.5244 (0.5256)\n",
      "Epoch: [10][400/792]\tTime 1.843 (716.673)\tETA 0:12:02\tLoss 0.5217 (0.5256)\n",
      "Epoch: [10][405/792]\tTime 1.798 (725.863)\tETA 0:11:35\tLoss 0.5274 (0.5256)\n",
      "Epoch: [10][410/792]\tTime 1.767 (734.733)\tETA 0:11:14\tLoss 0.5243 (0.5256)\n",
      "Epoch: [10][415/792]\tTime 1.895 (743.665)\tETA 0:11:54\tLoss 0.5252 (0.5256)\n",
      "Epoch: [10][420/792]\tTime 1.746 (752.664)\tETA 0:10:49\tLoss 0.5271 (0.5256)\n",
      "Epoch: [10][425/792]\tTime 1.802 (761.765)\tETA 0:11:01\tLoss 0.5252 (0.5256)\n",
      "Epoch: [10][430/792]\tTime 1.775 (770.722)\tETA 0:10:42\tLoss 0.5254 (0.5256)\n",
      "Epoch: [10][435/792]\tTime 1.850 (779.663)\tETA 0:11:00\tLoss 0.5259 (0.5256)\n",
      "Epoch: [10][440/792]\tTime 1.795 (788.508)\tETA 0:10:31\tLoss 0.5262 (0.5256)\n",
      "Epoch: [10][445/792]\tTime 1.752 (797.558)\tETA 0:10:07\tLoss 0.5246 (0.5256)\n",
      "Epoch: [10][450/792]\tTime 1.790 (806.522)\tETA 0:10:12\tLoss 0.5277 (0.5256)\n",
      "Epoch: [10][455/792]\tTime 1.811 (815.528)\tETA 0:10:10\tLoss 0.5248 (0.5255)\n",
      "Epoch: [10][460/792]\tTime 1.789 (824.483)\tETA 0:09:54\tLoss 0.5231 (0.5255)\n",
      "Epoch: [10][465/792]\tTime 1.791 (833.308)\tETA 0:09:45\tLoss 0.5315 (0.5255)\n",
      "Epoch: [10][470/792]\tTime 1.800 (842.126)\tETA 0:09:39\tLoss 0.5228 (0.5255)\n",
      "Epoch: [10][475/792]\tTime 1.775 (850.947)\tETA 0:09:22\tLoss 0.5265 (0.5255)\n",
      "Epoch: [10][480/792]\tTime 1.741 (859.788)\tETA 0:09:03\tLoss 0.5276 (0.5255)\n",
      "Epoch: [10][485/792]\tTime 1.790 (868.787)\tETA 0:09:09\tLoss 0.5234 (0.5255)\n",
      "Epoch: [10][490/792]\tTime 1.789 (877.697)\tETA 0:09:00\tLoss 0.5228 (0.5255)\n",
      "Epoch: [10][495/792]\tTime 1.787 (886.630)\tETA 0:08:50\tLoss 0.5257 (0.5255)\n",
      "Epoch: [10][500/792]\tTime 1.909 (895.808)\tETA 0:09:17\tLoss 0.5247 (0.5255)\n",
      "Epoch: [10][505/792]\tTime 1.759 (904.628)\tETA 0:08:24\tLoss 0.5282 (0.5255)\n",
      "Epoch: [10][510/792]\tTime 1.725 (913.433)\tETA 0:08:06\tLoss 0.5307 (0.5255)\n",
      "Epoch: [10][515/792]\tTime 1.765 (922.464)\tETA 0:08:08\tLoss 0.5263 (0.5255)\n",
      "Epoch: [10][520/792]\tTime 1.817 (931.421)\tETA 0:08:14\tLoss 0.5260 (0.5255)\n",
      "Epoch: [10][525/792]\tTime 1.816 (940.285)\tETA 0:08:04\tLoss 0.5289 (0.5255)\n",
      "Epoch: [10][530/792]\tTime 1.738 (949.145)\tETA 0:07:35\tLoss 0.5270 (0.5255)\n",
      "Epoch: [10][535/792]\tTime 1.758 (958.092)\tETA 0:07:31\tLoss 0.5251 (0.5256)\n",
      "Epoch: [10][540/792]\tTime 1.786 (966.858)\tETA 0:07:29\tLoss 0.5250 (0.5256)\n",
      "Epoch: [10][545/792]\tTime 1.764 (975.759)\tETA 0:07:15\tLoss 0.5235 (0.5256)\n",
      "Epoch: [10][550/792]\tTime 1.827 (984.621)\tETA 0:07:22\tLoss 0.5286 (0.5256)\n",
      "Epoch: [10][555/792]\tTime 1.808 (993.491)\tETA 0:07:08\tLoss 0.5246 (0.5256)\n",
      "Epoch: [10][560/792]\tTime 1.777 (1002.267)\tETA 0:06:52\tLoss 0.5258 (0.5256)\n",
      "Epoch: [10][565/792]\tTime 1.778 (1011.412)\tETA 0:06:43\tLoss 0.5243 (0.5256)\n",
      "Epoch: [10][570/792]\tTime 1.695 (1020.309)\tETA 0:06:16\tLoss 0.5238 (0.5256)\n",
      "Epoch: [10][575/792]\tTime 1.797 (1029.146)\tETA 0:06:29\tLoss 0.5256 (0.5256)\n",
      "Epoch: [10][580/792]\tTime 1.746 (1038.056)\tETA 0:06:10\tLoss 0.5251 (0.5256)\n",
      "Epoch: [10][585/792]\tTime 1.737 (1046.733)\tETA 0:05:59\tLoss 0.5242 (0.5255)\n",
      "Epoch: [10][590/792]\tTime 1.841 (1055.639)\tETA 0:06:11\tLoss 0.5248 (0.5255)\n",
      "Epoch: [10][595/792]\tTime 1.770 (1064.561)\tETA 0:05:48\tLoss 0.5226 (0.5255)\n",
      "Epoch: [10][600/792]\tTime 1.848 (1073.604)\tETA 0:05:54\tLoss 0.5251 (0.5255)\n",
      "Epoch: [10][605/792]\tTime 1.762 (1082.720)\tETA 0:05:29\tLoss 0.5235 (0.5255)\n",
      "Epoch: [10][610/792]\tTime 1.839 (1091.648)\tETA 0:05:34\tLoss 0.5226 (0.5255)\n",
      "Epoch: [10][615/792]\tTime 1.764 (1100.489)\tETA 0:05:12\tLoss 0.5235 (0.5255)\n",
      "Epoch: [10][620/792]\tTime 1.736 (1109.291)\tETA 0:04:58\tLoss 0.5247 (0.5255)\n",
      "Epoch: [10][625/792]\tTime 1.762 (1118.121)\tETA 0:04:54\tLoss 0.5243 (0.5255)\n",
      "Epoch: [10][630/792]\tTime 1.775 (1127.003)\tETA 0:04:47\tLoss 0.5227 (0.5254)\n",
      "Epoch: [10][635/792]\tTime 1.759 (1136.000)\tETA 0:04:36\tLoss 0.5221 (0.5254)\n",
      "Epoch: [10][640/792]\tTime 1.811 (1145.100)\tETA 0:04:35\tLoss 0.5256 (0.5254)\n",
      "Epoch: [10][645/792]\tTime 1.828 (1154.152)\tETA 0:04:28\tLoss 0.5267 (0.5254)\n",
      "Epoch: [10][650/792]\tTime 2.310 (1163.810)\tETA 0:05:28\tLoss 0.5257 (0.5254)\n",
      "Epoch: [10][655/792]\tTime 1.869 (1172.815)\tETA 0:04:16\tLoss 0.5284 (0.5254)\n",
      "Epoch: [10][660/792]\tTime 1.734 (1181.653)\tETA 0:03:48\tLoss 0.5238 (0.5254)\n",
      "Epoch: [10][665/792]\tTime 1.752 (1190.460)\tETA 0:03:42\tLoss 0.5284 (0.5254)\n",
      "Epoch: [10][670/792]\tTime 1.778 (1199.471)\tETA 0:03:36\tLoss 0.5254 (0.5254)\n",
      "Epoch: [10][675/792]\tTime 1.875 (1208.682)\tETA 0:03:39\tLoss 0.5258 (0.5254)\n",
      "Epoch: [10][680/792]\tTime 1.829 (1217.903)\tETA 0:03:24\tLoss 0.5245 (0.5254)\n",
      "Epoch: [10][685/792]\tTime 1.862 (1226.913)\tETA 0:03:19\tLoss 0.5259 (0.5254)\n",
      "Epoch: [10][690/792]\tTime 1.783 (1235.832)\tETA 0:03:01\tLoss 0.5241 (0.5254)\n",
      "Epoch: [10][695/792]\tTime 1.747 (1244.660)\tETA 0:02:49\tLoss 0.5246 (0.5254)\n",
      "Epoch: [10][700/792]\tTime 1.782 (1254.033)\tETA 0:02:43\tLoss 0.5247 (0.5254)\n",
      "Epoch: [10][705/792]\tTime 1.745 (1262.829)\tETA 0:02:31\tLoss 0.5243 (0.5254)\n",
      "Epoch: [10][710/792]\tTime 1.785 (1271.636)\tETA 0:02:26\tLoss 0.5252 (0.5254)\n",
      "Epoch: [10][715/792]\tTime 1.802 (1280.585)\tETA 0:02:18\tLoss 0.5245 (0.5254)\n",
      "Epoch: [10][720/792]\tTime 1.775 (1289.443)\tETA 0:02:07\tLoss 0.5244 (0.5254)\n",
      "Epoch: [10][725/792]\tTime 1.778 (1298.220)\tETA 0:01:59\tLoss 0.5260 (0.5254)\n",
      "Epoch: [10][730/792]\tTime 1.676 (1307.047)\tETA 0:01:43\tLoss 0.5269 (0.5254)\n",
      "Epoch: [10][735/792]\tTime 1.759 (1315.880)\tETA 0:01:40\tLoss 0.5250 (0.5254)\n",
      "Epoch: [10][740/792]\tTime 1.779 (1324.869)\tETA 0:01:32\tLoss 0.5241 (0.5254)\n",
      "Epoch: [10][745/792]\tTime 1.763 (1333.812)\tETA 0:01:22\tLoss 0.5241 (0.5254)\n",
      "Epoch: [10][750/792]\tTime 1.828 (1342.853)\tETA 0:01:16\tLoss 0.5241 (0.5254)\n",
      "Epoch: [10][755/792]\tTime 1.769 (1351.677)\tETA 0:01:05\tLoss 0.5239 (0.5254)\n",
      "Epoch: [10][760/792]\tTime 1.730 (1360.394)\tETA 0:00:55\tLoss 0.5224 (0.5254)\n",
      "Epoch: [10][765/792]\tTime 1.755 (1369.248)\tETA 0:00:47\tLoss 0.5242 (0.5254)\n",
      "Epoch: [10][770/792]\tTime 1.745 (1378.098)\tETA 0:00:38\tLoss 0.5244 (0.5253)\n",
      "Epoch: [10][775/792]\tTime 1.722 (1386.800)\tETA 0:00:29\tLoss 0.5231 (0.5253)\n",
      "Epoch: [10][780/792]\tTime 1.831 (1395.581)\tETA 0:00:21\tLoss 0.5255 (0.5253)\n",
      "Epoch: [10][785/792]\tTime 1.800 (1404.403)\tETA 0:00:12\tLoss 0.5245 (0.5253)\n",
      "Epoch: [10][790/792]\tTime 1.799 (1413.356)\tETA 0:00:03\tLoss 0.5247 (0.5253)\n",
      "Epoch: [11][0/792]\tTime 1.795 (1.795)\tETA 0:23:41\tLoss 0.5234 (0.5234)\n",
      "Epoch: [11][5/792]\tTime 1.819 (10.760)\tETA 0:23:51\tLoss 0.5245 (0.5242)\n",
      "Epoch: [11][10/792]\tTime 1.794 (19.865)\tETA 0:23:22\tLoss 0.5258 (0.5244)\n",
      "Epoch: [11][15/792]\tTime 1.826 (28.816)\tETA 0:23:39\tLoss 0.5270 (0.5248)\n",
      "Epoch: [11][20/792]\tTime 1.778 (37.637)\tETA 0:22:52\tLoss 0.5236 (0.5248)\n",
      "Epoch: [11][25/792]\tTime 1.789 (46.728)\tETA 0:22:51\tLoss 0.5260 (0.5247)\n",
      "Epoch: [11][30/792]\tTime 1.780 (55.795)\tETA 0:22:36\tLoss 0.5219 (0.5244)\n",
      "Epoch: [11][35/792]\tTime 1.769 (64.606)\tETA 0:22:19\tLoss 0.5228 (0.5243)\n",
      "Epoch: [11][40/792]\tTime 1.776 (73.669)\tETA 0:22:15\tLoss 0.5193 (0.5241)\n",
      "Epoch: [11][45/792]\tTime 1.711 (82.471)\tETA 0:21:18\tLoss 0.5253 (0.5240)\n",
      "Epoch: [11][50/792]\tTime 1.760 (91.336)\tETA 0:21:46\tLoss 0.5242 (0.5240)\n",
      "Epoch: [11][55/792]\tTime 1.815 (100.451)\tETA 0:22:17\tLoss 0.5237 (0.5240)\n",
      "Epoch: [11][60/792]\tTime 1.759 (109.366)\tETA 0:21:27\tLoss 0.5239 (0.5240)\n",
      "Epoch: [11][65/792]\tTime 1.788 (118.296)\tETA 0:21:40\tLoss 0.5238 (0.5239)\n",
      "Epoch: [11][70/792]\tTime 1.785 (127.147)\tETA 0:21:28\tLoss 0.5226 (0.5240)\n",
      "Epoch: [11][75/792]\tTime 1.851 (136.381)\tETA 0:22:07\tLoss 0.5268 (0.5241)\n",
      "Epoch: [11][80/792]\tTime 1.760 (145.291)\tETA 0:20:53\tLoss 0.5254 (0.5241)\n",
      "Epoch: [11][85/792]\tTime 1.745 (154.242)\tETA 0:20:33\tLoss 0.5266 (0.5242)\n",
      "Epoch: [11][90/792]\tTime 1.754 (163.090)\tETA 0:20:31\tLoss 0.5238 (0.5242)\n",
      "Epoch: [11][95/792]\tTime 1.699 (172.037)\tETA 0:19:44\tLoss 0.5267 (0.5242)\n",
      "Epoch: [11][100/792]\tTime 1.776 (180.907)\tETA 0:20:29\tLoss 0.5268 (0.5243)\n",
      "Epoch: [11][105/792]\tTime 1.817 (190.098)\tETA 0:20:48\tLoss 0.5243 (0.5243)\n",
      "Epoch: [11][110/792]\tTime 1.815 (199.785)\tETA 0:20:37\tLoss 0.5225 (0.5243)\n",
      "Epoch: [11][115/792]\tTime 1.751 (208.791)\tETA 0:19:45\tLoss 0.5207 (0.5243)\n",
      "Epoch: [11][120/792]\tTime 1.780 (217.831)\tETA 0:19:56\tLoss 0.5235 (0.5243)\n",
      "Epoch: [11][125/792]\tTime 1.795 (226.702)\tETA 0:19:57\tLoss 0.5275 (0.5243)\n",
      "Epoch: [11][130/792]\tTime 1.753 (235.524)\tETA 0:19:20\tLoss 0.5255 (0.5244)\n",
      "Epoch: [11][135/792]\tTime 1.812 (244.533)\tETA 0:19:50\tLoss 0.5281 (0.5246)\n",
      "Epoch: [11][140/792]\tTime 1.799 (253.386)\tETA 0:19:32\tLoss 0.5309 (0.5247)\n",
      "Epoch: [11][145/792]\tTime 1.773 (262.323)\tETA 0:19:07\tLoss 0.5250 (0.5247)\n",
      "Epoch: [11][150/792]\tTime 1.782 (271.314)\tETA 0:19:04\tLoss 0.5237 (0.5247)\n",
      "Epoch: [11][155/792]\tTime 1.797 (280.277)\tETA 0:19:04\tLoss 0.5267 (0.5247)\n",
      "Epoch: [11][160/792]\tTime 1.789 (289.241)\tETA 0:18:50\tLoss 0.5247 (0.5247)\n",
      "Epoch: [11][165/792]\tTime 1.822 (298.040)\tETA 0:19:02\tLoss 0.5241 (0.5247)\n",
      "Epoch: [11][170/792]\tTime 1.735 (306.963)\tETA 0:17:58\tLoss 0.5236 (0.5246)\n",
      "Epoch: [11][175/792]\tTime 1.788 (315.925)\tETA 0:18:23\tLoss 0.5222 (0.5246)\n",
      "Epoch: [11][180/792]\tTime 1.781 (324.731)\tETA 0:18:09\tLoss 0.5219 (0.5246)\n",
      "Epoch: [11][185/792]\tTime 1.800 (333.565)\tETA 0:18:12\tLoss 0.5247 (0.5246)\n",
      "Epoch: [11][190/792]\tTime 1.866 (342.871)\tETA 0:18:43\tLoss 0.5207 (0.5245)\n",
      "Epoch: [11][195/792]\tTime 1.667 (351.557)\tETA 0:16:34\tLoss 0.5234 (0.5245)\n",
      "Epoch: [11][200/792]\tTime 1.747 (360.547)\tETA 0:17:14\tLoss 0.5239 (0.5244)\n",
      "Epoch: [11][205/792]\tTime 1.833 (369.634)\tETA 0:17:56\tLoss 0.5256 (0.5244)\n",
      "Epoch: [11][210/792]\tTime 1.771 (378.603)\tETA 0:17:10\tLoss 0.5220 (0.5244)\n",
      "Epoch: [11][215/792]\tTime 1.796 (387.668)\tETA 0:17:16\tLoss 0.5224 (0.5244)\n",
      "Epoch: [11][220/792]\tTime 1.845 (396.692)\tETA 0:17:35\tLoss 0.5242 (0.5244)\n",
      "Epoch: [11][225/792]\tTime 1.859 (405.647)\tETA 0:17:34\tLoss 0.5257 (0.5244)\n",
      "Epoch: [11][230/792]\tTime 1.803 (414.609)\tETA 0:16:53\tLoss 0.5280 (0.5244)\n",
      "Epoch: [11][235/792]\tTime 1.771 (423.549)\tETA 0:16:26\tLoss 0.5241 (0.5245)\n",
      "Epoch: [11][240/792]\tTime 1.740 (432.435)\tETA 0:16:00\tLoss 0.5253 (0.5245)\n",
      "Epoch: [11][245/792]\tTime 1.818 (441.338)\tETA 0:16:34\tLoss 0.5256 (0.5245)\n",
      "Epoch: [11][250/792]\tTime 1.827 (450.192)\tETA 0:16:29\tLoss 0.5265 (0.5245)\n",
      "Epoch: [11][255/792]\tTime 1.735 (459.236)\tETA 0:15:31\tLoss 0.5256 (0.5245)\n",
      "Epoch: [11][260/792]\tTime 1.753 (468.190)\tETA 0:15:32\tLoss 0.5229 (0.5245)\n",
      "Epoch: [11][265/792]\tTime 1.770 (477.108)\tETA 0:15:33\tLoss 0.5252 (0.5245)\n",
      "Epoch: [11][270/792]\tTime 1.828 (486.187)\tETA 0:15:54\tLoss 0.5259 (0.5245)\n",
      "Epoch: [11][275/792]\tTime 1.804 (495.088)\tETA 0:15:32\tLoss 0.5270 (0.5245)\n",
      "Epoch: [11][280/792]\tTime 1.733 (504.042)\tETA 0:14:47\tLoss 0.5260 (0.5245)\n",
      "Epoch: [11][285/792]\tTime 1.772 (512.957)\tETA 0:14:58\tLoss 0.5239 (0.5245)\n",
      "Epoch: [11][290/792]\tTime 1.755 (521.791)\tETA 0:14:41\tLoss 0.5211 (0.5245)\n",
      "Epoch: [11][295/792]\tTime 1.744 (530.628)\tETA 0:14:26\tLoss 0.5275 (0.5245)\n",
      "Epoch: [11][300/792]\tTime 1.804 (539.982)\tETA 0:14:47\tLoss 0.5276 (0.5245)\n",
      "Epoch: [11][305/792]\tTime 1.766 (548.835)\tETA 0:14:20\tLoss 0.5222 (0.5245)\n",
      "Epoch: [11][310/792]\tTime 1.758 (557.660)\tETA 0:14:07\tLoss 0.5245 (0.5245)\n",
      "Epoch: [11][315/792]\tTime 1.723 (566.417)\tETA 0:13:41\tLoss 0.5253 (0.5245)\n",
      "Epoch: [11][320/792]\tTime 1.785 (575.311)\tETA 0:14:02\tLoss 0.5225 (0.5245)\n",
      "Epoch: [11][325/792]\tTime 1.737 (584.212)\tETA 0:13:30\tLoss 0.5240 (0.5245)\n",
      "Epoch: [11][330/792]\tTime 1.801 (593.036)\tETA 0:13:52\tLoss 0.5243 (0.5245)\n",
      "Epoch: [11][335/792]\tTime 1.720 (601.922)\tETA 0:13:05\tLoss 0.5240 (0.5245)\n",
      "Epoch: [11][340/792]\tTime 1.826 (610.867)\tETA 0:13:45\tLoss 0.5235 (0.5245)\n",
      "Epoch: [11][345/792]\tTime 1.705 (619.668)\tETA 0:12:42\tLoss 0.5230 (0.5245)\n",
      "Epoch: [11][350/792]\tTime 1.841 (628.629)\tETA 0:13:33\tLoss 0.5238 (0.5245)\n",
      "Epoch: [11][355/792]\tTime 1.795 (637.660)\tETA 0:13:04\tLoss 0.5253 (0.5245)\n",
      "Epoch: [11][360/792]\tTime 1.743 (646.624)\tETA 0:12:32\tLoss 0.5225 (0.5245)\n",
      "Epoch: [11][365/792]\tTime 1.751 (655.654)\tETA 0:12:27\tLoss 0.5225 (0.5245)\n",
      "Epoch: [11][370/792]\tTime 1.824 (664.696)\tETA 0:12:49\tLoss 0.5214 (0.5244)\n",
      "Epoch: [11][375/792]\tTime 1.777 (673.511)\tETA 0:12:20\tLoss 0.5231 (0.5245)\n",
      "Epoch: [11][380/792]\tTime 1.774 (682.454)\tETA 0:12:10\tLoss 0.5238 (0.5245)\n",
      "Epoch: [11][385/792]\tTime 1.831 (691.427)\tETA 0:12:25\tLoss 0.5237 (0.5245)\n",
      "Epoch: [11][390/792]\tTime 1.820 (700.278)\tETA 0:12:11\tLoss 0.5218 (0.5244)\n",
      "Epoch: [11][395/792]\tTime 1.832 (709.836)\tETA 0:12:07\tLoss 0.5235 (0.5244)\n",
      "Epoch: [11][400/792]\tTime 1.780 (718.693)\tETA 0:11:37\tLoss 0.5213 (0.5244)\n",
      "Epoch: [11][405/792]\tTime 1.725 (727.437)\tETA 0:11:07\tLoss 0.5219 (0.5244)\n",
      "Epoch: [11][410/792]\tTime 1.828 (736.334)\tETA 0:11:38\tLoss 0.5262 (0.5244)\n",
      "Epoch: [11][415/792]\tTime 1.722 (745.159)\tETA 0:10:49\tLoss 0.5266 (0.5244)\n",
      "Epoch: [11][420/792]\tTime 1.742 (754.034)\tETA 0:10:48\tLoss 0.5299 (0.5244)\n",
      "Epoch: [11][425/792]\tTime 1.734 (762.837)\tETA 0:10:36\tLoss 0.5248 (0.5244)\n",
      "Epoch: [11][430/792]\tTime 1.704 (771.554)\tETA 0:10:16\tLoss 0.5268 (0.5244)\n",
      "Epoch: [11][435/792]\tTime 1.750 (780.276)\tETA 0:10:24\tLoss 0.5246 (0.5244)\n",
      "Epoch: [11][440/792]\tTime 1.727 (789.036)\tETA 0:10:07\tLoss 0.5269 (0.5245)\n",
      "Epoch: [11][445/792]\tTime 1.757 (798.067)\tETA 0:10:09\tLoss 0.5237 (0.5245)\n",
      "Epoch: [11][450/792]\tTime 1.740 (806.999)\tETA 0:09:54\tLoss 0.5258 (0.5245)\n",
      "Epoch: [11][455/792]\tTime 1.801 (815.937)\tETA 0:10:06\tLoss 0.5251 (0.5245)\n",
      "Epoch: [11][460/792]\tTime 1.749 (824.797)\tETA 0:09:40\tLoss 0.5241 (0.5244)\n",
      "Epoch: [11][465/792]\tTime 1.822 (833.705)\tETA 0:09:55\tLoss 0.5231 (0.5244)\n",
      "Epoch: [11][470/792]\tTime 1.806 (842.669)\tETA 0:09:41\tLoss 0.5237 (0.5244)\n",
      "Epoch: [11][475/792]\tTime 1.770 (851.465)\tETA 0:09:21\tLoss 0.5261 (0.5245)\n",
      "Epoch: [11][480/792]\tTime 1.818 (860.473)\tETA 0:09:27\tLoss 0.5225 (0.5245)\n",
      "Epoch: [11][485/792]\tTime 1.758 (869.422)\tETA 0:08:59\tLoss 0.5224 (0.5245)\n",
      "Epoch: [11][490/792]\tTime 1.744 (878.261)\tETA 0:08:46\tLoss 0.5215 (0.5244)\n",
      "Epoch: [11][495/792]\tTime 1.785 (887.286)\tETA 0:08:50\tLoss 0.5273 (0.5245)\n",
      "Epoch: [11][500/792]\tTime 1.804 (896.275)\tETA 0:08:46\tLoss 0.5272 (0.5245)\n",
      "Epoch: [11][505/792]\tTime 1.808 (905.262)\tETA 0:08:38\tLoss 0.5322 (0.5245)\n",
      "Epoch: [11][510/792]\tTime 1.810 (913.941)\tETA 0:08:30\tLoss 0.5286 (0.5246)\n",
      "Epoch: [11][515/792]\tTime 1.745 (922.785)\tETA 0:08:03\tLoss 0.5265 (0.5246)\n",
      "Epoch: [11][520/792]\tTime 1.775 (931.662)\tETA 0:08:02\tLoss 0.5264 (0.5246)\n",
      "Epoch: [11][525/792]\tTime 1.720 (940.412)\tETA 0:07:39\tLoss 0.5245 (0.5246)\n",
      "Epoch: [11][530/792]\tTime 1.755 (949.357)\tETA 0:07:39\tLoss 0.5225 (0.5246)\n",
      "Epoch: [11][535/792]\tTime 1.762 (958.218)\tETA 0:07:32\tLoss 0.5248 (0.5246)\n",
      "Epoch: [11][540/792]\tTime 1.911 (967.154)\tETA 0:08:01\tLoss 0.5231 (0.5246)\n",
      "Epoch: [11][545/792]\tTime 1.776 (976.036)\tETA 0:07:18\tLoss 0.5232 (0.5246)\n",
      "Epoch: [11][550/792]\tTime 1.900 (985.061)\tETA 0:07:39\tLoss 0.5217 (0.5246)\n",
      "Epoch: [11][555/792]\tTime 1.743 (993.979)\tETA 0:06:52\tLoss 0.5254 (0.5245)\n",
      "Epoch: [11][560/792]\tTime 1.863 (1003.052)\tETA 0:07:12\tLoss 0.5262 (0.5245)\n",
      "Epoch: [11][565/792]\tTime 1.768 (1012.062)\tETA 0:06:41\tLoss 0.5248 (0.5245)\n",
      "Epoch: [11][570/792]\tTime 1.754 (1021.023)\tETA 0:06:29\tLoss 0.5240 (0.5245)\n",
      "Epoch: [11][575/792]\tTime 1.719 (1030.075)\tETA 0:06:13\tLoss 0.5243 (0.5245)\n",
      "Epoch: [11][580/792]\tTime 1.800 (1039.118)\tETA 0:06:21\tLoss 0.5250 (0.5245)\n",
      "Epoch: [11][585/792]\tTime 1.722 (1048.070)\tETA 0:05:56\tLoss 0.5238 (0.5245)\n",
      "Epoch: [11][590/792]\tTime 1.849 (1057.027)\tETA 0:06:13\tLoss 0.5273 (0.5245)\n",
      "Epoch: [11][595/792]\tTime 1.771 (1066.161)\tETA 0:05:48\tLoss 0.5241 (0.5245)\n",
      "Epoch: [11][600/792]\tTime 1.890 (1075.261)\tETA 0:06:02\tLoss 0.5262 (0.5246)\n",
      "Epoch: [11][605/792]\tTime 1.786 (1084.355)\tETA 0:05:33\tLoss 0.5235 (0.5246)\n",
      "Epoch: [11][610/792]\tTime 1.796 (1093.262)\tETA 0:05:26\tLoss 0.5229 (0.5246)\n",
      "Epoch: [11][615/792]\tTime 1.872 (1102.804)\tETA 0:05:31\tLoss 0.5255 (0.5246)\n",
      "Epoch: [11][620/792]\tTime 1.776 (1112.268)\tETA 0:05:05\tLoss 0.5219 (0.5246)\n",
      "Epoch: [11][625/792]\tTime 1.760 (1121.137)\tETA 0:04:53\tLoss 0.5201 (0.5245)\n",
      "Epoch: [11][630/792]\tTime 1.702 (1130.032)\tETA 0:04:35\tLoss 0.5239 (0.5245)\n",
      "Epoch: [11][635/792]\tTime 1.792 (1138.879)\tETA 0:04:41\tLoss 0.5239 (0.5245)\n",
      "Epoch: [11][640/792]\tTime 1.784 (1147.762)\tETA 0:04:31\tLoss 0.5233 (0.5245)\n",
      "Epoch: [11][645/792]\tTime 1.841 (1156.846)\tETA 0:04:30\tLoss 0.5246 (0.5245)\n",
      "Epoch: [11][650/792]\tTime 1.727 (1165.868)\tETA 0:04:05\tLoss 0.5296 (0.5245)\n",
      "Epoch: [11][655/792]\tTime 1.794 (1174.804)\tETA 0:04:05\tLoss 0.5227 (0.5245)\n",
      "Epoch: [11][660/792]\tTime 1.780 (1183.713)\tETA 0:03:54\tLoss 0.5227 (0.5245)\n",
      "Epoch: [11][665/792]\tTime 1.847 (1192.790)\tETA 0:03:54\tLoss 0.5226 (0.5245)\n",
      "Epoch: [11][670/792]\tTime 1.781 (1201.773)\tETA 0:03:37\tLoss 0.5244 (0.5245)\n",
      "Epoch: [11][675/792]\tTime 1.770 (1210.552)\tETA 0:03:27\tLoss 0.5224 (0.5245)\n",
      "Epoch: [11][680/792]\tTime 1.724 (1219.434)\tETA 0:03:13\tLoss 0.5267 (0.5245)\n",
      "Epoch: [11][685/792]\tTime 1.822 (1228.299)\tETA 0:03:14\tLoss 0.5234 (0.5245)\n",
      "Epoch: [11][690/792]\tTime 1.739 (1237.241)\tETA 0:02:57\tLoss 0.5246 (0.5245)\n",
      "Epoch: [11][695/792]\tTime 1.763 (1246.039)\tETA 0:02:50\tLoss 0.5247 (0.5245)\n",
      "Epoch: [11][700/792]\tTime 1.742 (1254.691)\tETA 0:02:40\tLoss 0.5266 (0.5245)\n",
      "Epoch: [11][705/792]\tTime 1.775 (1263.486)\tETA 0:02:34\tLoss 0.5253 (0.5245)\n",
      "Epoch: [11][710/792]\tTime 1.854 (1272.391)\tETA 0:02:31\tLoss 0.5203 (0.5245)\n",
      "Epoch: [11][715/792]\tTime 1.739 (1281.357)\tETA 0:02:13\tLoss 0.5280 (0.5245)\n",
      "Epoch: [11][720/792]\tTime 1.789 (1290.337)\tETA 0:02:08\tLoss 0.5260 (0.5245)\n",
      "Epoch: [11][725/792]\tTime 1.865 (1299.215)\tETA 0:02:04\tLoss 0.5250 (0.5245)\n",
      "Epoch: [11][730/792]\tTime 2.190 (1308.557)\tETA 0:02:15\tLoss 0.5242 (0.5245)\n",
      "Epoch: [11][735/792]\tTime 1.788 (1317.782)\tETA 0:01:41\tLoss 0.5226 (0.5245)\n",
      "Epoch: [11][740/792]\tTime 1.815 (1326.759)\tETA 0:01:34\tLoss 0.5244 (0.5245)\n",
      "Epoch: [11][745/792]\tTime 1.799 (1335.642)\tETA 0:01:24\tLoss 0.5260 (0.5245)\n",
      "Epoch: [11][750/792]\tTime 1.861 (1344.756)\tETA 0:01:18\tLoss 0.5260 (0.5245)\n",
      "Epoch: [11][755/792]\tTime 1.817 (1353.721)\tETA 0:01:07\tLoss 0.5241 (0.5245)\n",
      "Epoch: [11][760/792]\tTime 1.789 (1362.565)\tETA 0:00:57\tLoss 0.5227 (0.5245)\n",
      "Epoch: [11][765/792]\tTime 1.796 (1371.462)\tETA 0:00:48\tLoss 0.5245 (0.5245)\n",
      "Epoch: [11][770/792]\tTime 1.813 (1380.388)\tETA 0:00:39\tLoss 0.5234 (0.5245)\n",
      "Epoch: [11][775/792]\tTime 1.788 (1389.333)\tETA 0:00:30\tLoss 0.5232 (0.5245)\n",
      "Epoch: [11][780/792]\tTime 1.698 (1398.188)\tETA 0:00:20\tLoss 0.5240 (0.5245)\n",
      "Epoch: [11][785/792]\tTime 1.731 (1406.957)\tETA 0:00:12\tLoss 0.5228 (0.5245)\n",
      "Epoch: [11][790/792]\tTime 1.799 (1415.928)\tETA 0:00:03\tLoss 0.5263 (0.5245)\n",
      "Epoch: [12][0/792]\tTime 1.797 (1.797)\tETA 0:23:43\tLoss 0.5253 (0.5253)\n",
      "Epoch: [12][5/792]\tTime 1.771 (10.739)\tETA 0:23:13\tLoss 0.5234 (0.5234)\n",
      "Epoch: [12][10/792]\tTime 1.749 (19.684)\tETA 0:22:48\tLoss 0.5256 (0.5230)\n",
      "Epoch: [12][15/792]\tTime 1.732 (28.431)\tETA 0:22:25\tLoss 0.5223 (0.5231)\n",
      "Epoch: [12][20/792]\tTime 1.803 (37.253)\tETA 0:23:12\tLoss 0.5246 (0.5232)\n",
      "Epoch: [12][25/792]\tTime 1.793 (46.112)\tETA 0:22:54\tLoss 0.5219 (0.5231)\n",
      "Epoch: [12][30/792]\tTime 1.741 (54.914)\tETA 0:22:06\tLoss 0.5228 (0.5230)\n",
      "Epoch: [12][35/792]\tTime 1.794 (63.851)\tETA 0:22:38\tLoss 0.5210 (0.5230)\n",
      "Epoch: [12][40/792]\tTime 1.826 (72.801)\tETA 0:22:53\tLoss 0.5225 (0.5231)\n",
      "Epoch: [12][45/792]\tTime 1.765 (81.623)\tETA 0:21:58\tLoss 0.5267 (0.5233)\n",
      "Epoch: [12][50/792]\tTime 1.781 (90.533)\tETA 0:22:01\tLoss 0.5237 (0.5234)\n",
      "Epoch: [12][55/792]\tTime 1.755 (99.590)\tETA 0:21:33\tLoss 0.5243 (0.5236)\n",
      "Epoch: [12][60/792]\tTime 1.819 (108.449)\tETA 0:22:11\tLoss 0.5239 (0.5235)\n",
      "Epoch: [12][65/792]\tTime 1.733 (117.337)\tETA 0:21:00\tLoss 0.5224 (0.5235)\n",
      "Epoch: [12][70/792]\tTime 1.793 (126.493)\tETA 0:21:34\tLoss 0.5219 (0.5234)\n",
      "Epoch: [12][75/792]\tTime 1.796 (135.548)\tETA 0:21:27\tLoss 0.5258 (0.5236)\n",
      "Epoch: [12][80/792]\tTime 1.803 (144.487)\tETA 0:21:23\tLoss 0.5258 (0.5238)\n",
      "Epoch: [12][85/792]\tTime 1.763 (153.521)\tETA 0:20:46\tLoss 0.5226 (0.5238)\n",
      "Epoch: [12][90/792]\tTime 1.810 (162.646)\tETA 0:21:10\tLoss 0.5230 (0.5238)\n",
      "Epoch: [12][95/792]\tTime 1.835 (172.017)\tETA 0:21:18\tLoss 0.5209 (0.5237)\n",
      "Epoch: [12][100/792]\tTime 1.810 (180.951)\tETA 0:20:52\tLoss 0.5226 (0.5237)\n",
      "Epoch: [12][105/792]\tTime 1.770 (190.079)\tETA 0:20:15\tLoss 0.5233 (0.5237)\n",
      "Epoch: [12][110/792]\tTime 1.838 (199.072)\tETA 0:20:53\tLoss 0.5255 (0.5237)\n",
      "Epoch: [12][115/792]\tTime 1.803 (207.946)\tETA 0:20:20\tLoss 0.5224 (0.5237)\n",
      "Epoch: [12][120/792]\tTime 1.766 (216.771)\tETA 0:19:46\tLoss 0.5258 (0.5238)\n",
      "Epoch: [12][125/792]\tTime 1.776 (225.616)\tETA 0:19:44\tLoss 0.5252 (0.5239)\n",
      "Epoch: [12][130/792]\tTime 1.846 (234.487)\tETA 0:20:22\tLoss 0.5232 (0.5239)\n",
      "Epoch: [12][135/792]\tTime 1.846 (243.660)\tETA 0:20:12\tLoss 0.5258 (0.5239)\n",
      "Epoch: [12][140/792]\tTime 1.818 (252.728)\tETA 0:19:45\tLoss 0.5256 (0.5239)\n",
      "Epoch: [12][145/792]\tTime 1.697 (261.633)\tETA 0:18:17\tLoss 0.5231 (0.5239)\n",
      "Epoch: [12][150/792]\tTime 1.682 (270.461)\tETA 0:18:00\tLoss 0.5226 (0.5239)\n",
      "Epoch: [12][155/792]\tTime 1.735 (279.305)\tETA 0:18:24\tLoss 0.5234 (0.5239)\n",
      "Epoch: [12][160/792]\tTime 1.715 (288.243)\tETA 0:18:04\tLoss 0.5208 (0.5238)\n",
      "Epoch: [12][165/792]\tTime 1.955 (297.471)\tETA 0:20:25\tLoss 0.5229 (0.5238)\n",
      "Epoch: [12][170/792]\tTime 1.686 (306.361)\tETA 0:17:28\tLoss 0.5235 (0.5239)\n",
      "Epoch: [12][175/792]\tTime 1.945 (315.498)\tETA 0:20:00\tLoss 0.5274 (0.5239)\n",
      "Epoch: [12][180/792]\tTime 1.788 (324.396)\tETA 0:18:14\tLoss 0.5226 (0.5239)\n",
      "Epoch: [12][185/792]\tTime 1.832 (333.368)\tETA 0:18:32\tLoss 0.5271 (0.5239)\n",
      "Epoch: [12][190/792]\tTime 1.886 (342.543)\tETA 0:18:55\tLoss 0.5223 (0.5239)\n",
      "Epoch: [12][195/792]\tTime 1.815 (351.442)\tETA 0:18:03\tLoss 0.5226 (0.5239)\n",
      "Epoch: [12][200/792]\tTime 1.761 (360.588)\tETA 0:17:22\tLoss 0.5232 (0.5239)\n",
      "Epoch: [12][205/792]\tTime 1.805 (369.571)\tETA 0:17:39\tLoss 0.5212 (0.5239)\n",
      "Epoch: [12][210/792]\tTime 1.782 (378.449)\tETA 0:17:17\tLoss 0.5235 (0.5239)\n",
      "Epoch: [12][215/792]\tTime 1.833 (387.386)\tETA 0:17:37\tLoss 0.5258 (0.5239)\n",
      "Epoch: [12][220/792]\tTime 1.796 (396.308)\tETA 0:17:07\tLoss 0.5241 (0.5239)\n",
      "Epoch: [12][225/792]\tTime 1.846 (405.272)\tETA 0:17:26\tLoss 0.5250 (0.5239)\n",
      "Epoch: [12][230/792]\tTime 1.823 (414.345)\tETA 0:17:04\tLoss 0.5261 (0.5239)\n",
      "Epoch: [12][235/792]\tTime 1.806 (423.176)\tETA 0:16:45\tLoss 0.5229 (0.5239)\n",
      "Epoch: [12][240/792]\tTime 1.726 (432.339)\tETA 0:15:52\tLoss 0.5220 (0.5239)\n",
      "Epoch: [12][245/792]\tTime 1.833 (441.195)\tETA 0:16:42\tLoss 0.5232 (0.5238)\n",
      "Epoch: [12][250/792]\tTime 1.707 (450.003)\tETA 0:15:25\tLoss 0.5243 (0.5238)\n",
      "Epoch: [12][255/792]\tTime 1.844 (458.879)\tETA 0:16:30\tLoss 0.5241 (0.5238)\n",
      "Epoch: [12][260/792]\tTime 1.765 (467.751)\tETA 0:15:39\tLoss 0.5207 (0.5238)\n",
      "Epoch: [12][265/792]\tTime 1.732 (476.657)\tETA 0:15:12\tLoss 0.5211 (0.5238)\n",
      "Epoch: [12][270/792]\tTime 1.791 (485.655)\tETA 0:15:34\tLoss 0.5230 (0.5238)\n",
      "Epoch: [12][275/792]\tTime 1.970 (494.755)\tETA 0:16:58\tLoss 0.5234 (0.5238)\n",
      "Epoch: [12][280/792]\tTime 1.762 (503.542)\tETA 0:15:02\tLoss 0.5241 (0.5237)\n",
      "Epoch: [12][285/792]\tTime 1.838 (512.377)\tETA 0:15:31\tLoss 0.5231 (0.5237)\n",
      "Epoch: [12][290/792]\tTime 1.732 (521.387)\tETA 0:14:29\tLoss 0.5247 (0.5237)\n",
      "Epoch: [12][295/792]\tTime 1.801 (530.300)\tETA 0:14:54\tLoss 0.5246 (0.5237)\n",
      "Epoch: [12][300/792]\tTime 1.807 (539.663)\tETA 0:14:49\tLoss 0.5254 (0.5237)\n",
      "Epoch: [12][305/792]\tTime 1.755 (548.603)\tETA 0:14:14\tLoss 0.5243 (0.5237)\n",
      "Epoch: [12][310/792]\tTime 1.843 (557.486)\tETA 0:14:48\tLoss 0.5266 (0.5237)\n",
      "Epoch: [12][315/792]\tTime 1.822 (566.531)\tETA 0:14:29\tLoss 0.5274 (0.5237)\n",
      "Epoch: [12][320/792]\tTime 1.735 (575.495)\tETA 0:13:38\tLoss 0.5224 (0.5237)\n",
      "Epoch: [12][325/792]\tTime 1.750 (584.570)\tETA 0:13:37\tLoss 0.5231 (0.5237)\n",
      "Epoch: [12][330/792]\tTime 1.772 (593.547)\tETA 0:13:38\tLoss 0.5242 (0.5237)\n",
      "Epoch: [12][335/792]\tTime 1.761 (602.359)\tETA 0:13:24\tLoss 0.5240 (0.5237)\n",
      "Epoch: [12][340/792]\tTime 1.807 (611.325)\tETA 0:13:36\tLoss 0.5221 (0.5237)\n",
      "Epoch: [12][345/792]\tTime 1.761 (620.189)\tETA 0:13:07\tLoss 0.5282 (0.5238)\n",
      "Epoch: [12][350/792]\tTime 1.716 (628.960)\tETA 0:12:38\tLoss 0.5241 (0.5238)\n",
      "Epoch: [12][355/792]\tTime 1.730 (637.799)\tETA 0:12:35\tLoss 0.5270 (0.5238)\n",
      "Epoch: [12][360/792]\tTime 1.701 (646.636)\tETA 0:12:14\tLoss 0.5269 (0.5238)\n",
      "Epoch: [12][365/792]\tTime 1.821 (655.602)\tETA 0:12:57\tLoss 0.5268 (0.5239)\n",
      "Epoch: [12][370/792]\tTime 1.846 (664.571)\tETA 0:12:58\tLoss 0.5288 (0.5239)\n",
      "Epoch: [12][375/792]\tTime 1.775 (673.456)\tETA 0:12:19\tLoss 0.5299 (0.5240)\n",
      "Epoch: [12][380/792]\tTime 1.708 (682.192)\tETA 0:11:43\tLoss 0.5264 (0.5240)\n",
      "Epoch: [12][385/792]\tTime 1.842 (691.066)\tETA 0:12:29\tLoss 0.5236 (0.5240)\n",
      "Epoch: [12][390/792]\tTime 1.833 (699.992)\tETA 0:12:17\tLoss 0.5240 (0.5240)\n",
      "Epoch: [12][395/792]\tTime 1.763 (709.161)\tETA 0:11:39\tLoss 0.5238 (0.5240)\n",
      "Epoch: [12][400/792]\tTime 1.723 (717.864)\tETA 0:11:15\tLoss 0.5245 (0.5240)\n",
      "Epoch: [12][405/792]\tTime 1.773 (726.790)\tETA 0:11:26\tLoss 0.5235 (0.5240)\n",
      "Epoch: [12][410/792]\tTime 1.772 (735.852)\tETA 0:11:17\tLoss 0.5264 (0.5240)\n",
      "Epoch: [12][415/792]\tTime 1.777 (744.974)\tETA 0:11:10\tLoss 0.5347 (0.5241)\n",
      "Epoch: [12][420/792]\tTime 1.787 (753.934)\tETA 0:11:04\tLoss 0.5257 (0.5241)\n",
      "Epoch: [12][425/792]\tTime 1.822 (763.133)\tETA 0:11:08\tLoss 0.5211 (0.5241)\n",
      "Epoch: [12][430/792]\tTime 1.855 (772.320)\tETA 0:11:11\tLoss 0.5302 (0.5241)\n",
      "Epoch: [12][435/792]\tTime 1.758 (781.359)\tETA 0:10:27\tLoss 0.5232 (0.5241)\n",
      "Epoch: [12][440/792]\tTime 1.860 (790.281)\tETA 0:10:54\tLoss 0.5274 (0.5241)\n",
      "Epoch: [12][445/792]\tTime 1.761 (799.264)\tETA 0:10:10\tLoss 0.5266 (0.5241)\n",
      "Epoch: [12][450/792]\tTime 1.810 (808.172)\tETA 0:10:19\tLoss 0.5217 (0.5241)\n",
      "Epoch: [12][455/792]\tTime 1.777 (817.235)\tETA 0:09:58\tLoss 0.5246 (0.5241)\n",
      "Epoch: [12][460/792]\tTime 1.770 (826.778)\tETA 0:09:47\tLoss 0.5229 (0.5241)\n",
      "Epoch: [12][465/792]\tTime 1.824 (835.798)\tETA 0:09:56\tLoss 0.5263 (0.5241)\n",
      "Epoch: [12][470/792]\tTime 1.733 (844.709)\tETA 0:09:18\tLoss 0.5229 (0.5241)\n",
      "Epoch: [12][475/792]\tTime 1.748 (853.698)\tETA 0:09:14\tLoss 0.5230 (0.5241)\n",
      "Epoch: [12][480/792]\tTime 1.904 (862.617)\tETA 0:09:54\tLoss 0.5243 (0.5241)\n",
      "Epoch: [12][485/792]\tTime 1.763 (871.387)\tETA 0:09:01\tLoss 0.5238 (0.5241)\n",
      "Epoch: [12][490/792]\tTime 1.756 (880.325)\tETA 0:08:50\tLoss 0.5234 (0.5241)\n",
      "Epoch: [12][495/792]\tTime 1.746 (889.214)\tETA 0:08:38\tLoss 0.5215 (0.5241)\n",
      "Epoch: [12][500/792]\tTime 1.769 (898.086)\tETA 0:08:36\tLoss 0.5246 (0.5241)\n",
      "Epoch: [12][505/792]\tTime 1.818 (906.952)\tETA 0:08:41\tLoss 0.5213 (0.5240)\n",
      "Epoch: [12][510/792]\tTime 1.877 (915.965)\tETA 0:08:49\tLoss 0.5253 (0.5240)\n",
      "Epoch: [12][515/792]\tTime 1.789 (924.780)\tETA 0:08:15\tLoss 0.5253 (0.5241)\n",
      "Epoch: [12][520/792]\tTime 1.744 (933.780)\tETA 0:07:54\tLoss 0.5241 (0.5240)\n",
      "Epoch: [12][525/792]\tTime 1.734 (942.692)\tETA 0:07:43\tLoss 0.5230 (0.5240)\n",
      "Epoch: [12][530/792]\tTime 1.782 (951.654)\tETA 0:07:46\tLoss 0.5237 (0.5240)\n",
      "Epoch: [12][535/792]\tTime 1.745 (960.505)\tETA 0:07:28\tLoss 0.5224 (0.5240)\n",
      "Epoch: [12][540/792]\tTime 1.779 (969.442)\tETA 0:07:28\tLoss 0.5215 (0.5240)\n",
      "Epoch: [12][545/792]\tTime 1.829 (978.357)\tETA 0:07:31\tLoss 0.5222 (0.5240)\n",
      "Epoch: [12][550/792]\tTime 1.831 (987.466)\tETA 0:07:23\tLoss 0.5214 (0.5240)\n",
      "Epoch: [12][555/792]\tTime 1.766 (996.266)\tETA 0:06:58\tLoss 0.5215 (0.5240)\n",
      "Epoch: [12][560/792]\tTime 2.207 (1006.289)\tETA 0:08:31\tLoss 0.5210 (0.5239)\n",
      "Epoch: [12][565/792]\tTime 1.788 (1015.510)\tETA 0:06:45\tLoss 0.5218 (0.5239)\n",
      "Epoch: [12][570/792]\tTime 1.852 (1024.473)\tETA 0:06:51\tLoss 0.5230 (0.5239)\n",
      "Epoch: [12][575/792]\tTime 1.736 (1033.257)\tETA 0:06:16\tLoss 0.5221 (0.5239)\n",
      "Epoch: [12][580/792]\tTime 1.744 (1042.163)\tETA 0:06:09\tLoss 0.5261 (0.5239)\n",
      "Epoch: [12][585/792]\tTime 1.780 (1050.916)\tETA 0:06:08\tLoss 0.5203 (0.5239)\n",
      "Epoch: [12][590/792]\tTime 1.787 (1060.206)\tETA 0:06:00\tLoss 0.5229 (0.5240)\n",
      "Epoch: [12][595/792]\tTime 1.789 (1069.142)\tETA 0:05:52\tLoss 0.5285 (0.5240)\n",
      "Epoch: [12][600/792]\tTime 1.806 (1078.192)\tETA 0:05:46\tLoss 0.5314 (0.5240)\n",
      "Epoch: [12][605/792]\tTime 1.844 (1087.329)\tETA 0:05:44\tLoss 0.5265 (0.5240)\n",
      "Epoch: [12][610/792]\tTime 1.796 (1096.421)\tETA 0:05:26\tLoss 0.5217 (0.5241)\n",
      "Epoch: [12][615/792]\tTime 1.771 (1105.213)\tETA 0:05:13\tLoss 0.5267 (0.5241)\n",
      "Epoch: [12][620/792]\tTime 1.842 (1114.397)\tETA 0:05:16\tLoss 0.5235 (0.5241)\n",
      "Epoch: [12][625/792]\tTime 1.737 (1123.494)\tETA 0:04:50\tLoss 0.5254 (0.5240)\n",
      "Epoch: [12][630/792]\tTime 1.763 (1132.394)\tETA 0:04:45\tLoss 0.5227 (0.5240)\n",
      "Epoch: [12][635/792]\tTime 2.004 (1141.573)\tETA 0:05:14\tLoss 0.5208 (0.5240)\n",
      "Epoch: [12][640/792]\tTime 1.839 (1151.095)\tETA 0:04:39\tLoss 0.5239 (0.5240)\n",
      "Epoch: [12][645/792]\tTime 1.760 (1159.925)\tETA 0:04:18\tLoss 0.5236 (0.5240)\n",
      "Epoch: [12][650/792]\tTime 1.802 (1168.987)\tETA 0:04:15\tLoss 0.5196 (0.5240)\n",
      "Epoch: [12][655/792]\tTime 1.721 (1178.051)\tETA 0:03:55\tLoss 0.5223 (0.5240)\n",
      "Epoch: [12][660/792]\tTime 1.783 (1187.799)\tETA 0:03:55\tLoss 0.5249 (0.5240)\n",
      "Epoch: [12][665/792]\tTime 1.786 (1196.591)\tETA 0:03:46\tLoss 0.5221 (0.5240)\n",
      "Epoch: [12][670/792]\tTime 1.758 (1205.360)\tETA 0:03:34\tLoss 0.5233 (0.5240)\n",
      "Epoch: [12][675/792]\tTime 1.904 (1214.422)\tETA 0:03:42\tLoss 0.5219 (0.5239)\n",
      "Epoch: [12][680/792]\tTime 1.785 (1223.346)\tETA 0:03:19\tLoss 0.5225 (0.5239)\n",
      "Epoch: [12][685/792]\tTime 1.701 (1232.202)\tETA 0:03:01\tLoss 0.5258 (0.5239)\n",
      "Epoch: [12][690/792]\tTime 1.786 (1241.111)\tETA 0:03:02\tLoss 0.5242 (0.5239)\n",
      "Epoch: [12][695/792]\tTime 1.779 (1250.096)\tETA 0:02:52\tLoss 0.5249 (0.5239)\n",
      "Epoch: [12][700/792]\tTime 1.793 (1259.174)\tETA 0:02:44\tLoss 0.5212 (0.5239)\n",
      "Epoch: [12][705/792]\tTime 1.823 (1268.074)\tETA 0:02:38\tLoss 0.5218 (0.5239)\n",
      "Epoch: [12][710/792]\tTime 1.709 (1276.965)\tETA 0:02:20\tLoss 0.5204 (0.5239)\n",
      "Epoch: [12][715/792]\tTime 1.808 (1285.742)\tETA 0:02:19\tLoss 0.5239 (0.5239)\n",
      "Epoch: [12][720/792]\tTime 1.815 (1294.936)\tETA 0:02:10\tLoss 0.5241 (0.5239)\n",
      "Epoch: [12][725/792]\tTime 1.783 (1304.085)\tETA 0:01:59\tLoss 0.5215 (0.5239)\n",
      "Epoch: [12][730/792]\tTime 1.722 (1313.141)\tETA 0:01:46\tLoss 0.5230 (0.5239)\n",
      "Epoch: [12][735/792]\tTime 1.861 (1322.069)\tETA 0:01:46\tLoss 0.5227 (0.5239)\n",
      "Epoch: [12][740/792]\tTime 1.780 (1331.107)\tETA 0:01:32\tLoss 0.5220 (0.5239)\n",
      "Epoch: [12][745/792]\tTime 1.692 (1340.181)\tETA 0:01:19\tLoss 0.5274 (0.5239)\n",
      "Epoch: [12][750/792]\tTime 1.767 (1348.995)\tETA 0:01:14\tLoss 0.5235 (0.5238)\n",
      "Epoch: [12][755/792]\tTime 1.745 (1357.913)\tETA 0:01:04\tLoss 0.5228 (0.5238)\n",
      "Epoch: [12][760/792]\tTime 1.791 (1366.906)\tETA 0:00:57\tLoss 0.5219 (0.5238)\n",
      "Epoch: [12][765/792]\tTime 1.807 (1375.954)\tETA 0:00:48\tLoss 0.5235 (0.5238)\n",
      "Epoch: [12][770/792]\tTime 1.675 (1384.777)\tETA 0:00:36\tLoss 0.5251 (0.5238)\n",
      "Epoch: [12][775/792]\tTime 1.770 (1393.603)\tETA 0:00:30\tLoss 0.5227 (0.5238)\n",
      "Epoch: [12][780/792]\tTime 1.808 (1402.609)\tETA 0:00:21\tLoss 0.5207 (0.5238)\n",
      "Epoch: [12][785/792]\tTime 1.809 (1411.472)\tETA 0:00:12\tLoss 0.5230 (0.5238)\n",
      "Epoch: [12][790/792]\tTime 1.754 (1420.282)\tETA 0:00:03\tLoss 0.5235 (0.5238)\n",
      "Epoch: [13][0/792]\tTime 1.757 (1.757)\tETA 0:23:11\tLoss 0.5252 (0.5252)\n",
      "Epoch: [13][5/792]\tTime 1.821 (10.813)\tETA 0:23:53\tLoss 0.5248 (0.5250)\n",
      "Epoch: [13][10/792]\tTime 1.807 (19.732)\tETA 0:23:32\tLoss 0.5251 (0.5255)\n",
      "Epoch: [13][15/792]\tTime 1.737 (28.367)\tETA 0:22:29\tLoss 0.5245 (0.5259)\n",
      "Epoch: [13][20/792]\tTime 1.745 (37.386)\tETA 0:22:27\tLoss 0.5219 (0.5254)\n",
      "Epoch: [13][25/792]\tTime 1.774 (46.244)\tETA 0:22:41\tLoss 0.5236 (0.5250)\n",
      "Epoch: [13][30/792]\tTime 1.779 (55.166)\tETA 0:22:35\tLoss 0.5236 (0.5249)\n",
      "Epoch: [13][35/792]\tTime 1.835 (64.267)\tETA 0:23:09\tLoss 0.5237 (0.5247)\n",
      "Epoch: [13][40/792]\tTime 1.732 (73.039)\tETA 0:21:42\tLoss 0.5237 (0.5245)\n",
      "Epoch: [13][45/792]\tTime 1.772 (81.896)\tETA 0:22:03\tLoss 0.5245 (0.5245)\n",
      "Epoch: [13][50/792]\tTime 1.767 (90.930)\tETA 0:21:51\tLoss 0.5240 (0.5243)\n",
      "Epoch: [13][55/792]\tTime 1.854 (100.028)\tETA 0:22:46\tLoss 0.5214 (0.5242)\n",
      "Epoch: [13][60/792]\tTime 1.718 (108.853)\tETA 0:20:57\tLoss 0.5239 (0.5241)\n",
      "Epoch: [13][65/792]\tTime 1.825 (117.976)\tETA 0:22:07\tLoss 0.5222 (0.5240)\n",
      "Epoch: [13][70/792]\tTime 1.862 (127.086)\tETA 0:22:24\tLoss 0.5239 (0.5239)\n",
      "Epoch: [13][75/792]\tTime 1.915 (136.335)\tETA 0:22:53\tLoss 0.5218 (0.5238)\n",
      "Epoch: [13][80/792]\tTime 1.858 (145.580)\tETA 0:22:02\tLoss 0.5209 (0.5236)\n",
      "Epoch: [13][85/792]\tTime 1.732 (154.608)\tETA 0:20:24\tLoss 0.5229 (0.5237)\n",
      "Epoch: [13][90/792]\tTime 1.769 (163.648)\tETA 0:20:41\tLoss 0.5236 (0.5237)\n",
      "Epoch: [13][95/792]\tTime 1.862 (172.562)\tETA 0:21:37\tLoss 0.5232 (0.5237)\n",
      "Epoch: [13][100/792]\tTime 1.773 (181.751)\tETA 0:20:27\tLoss 0.5232 (0.5237)\n",
      "Epoch: [13][105/792]\tTime 1.712 (190.710)\tETA 0:19:36\tLoss 0.5225 (0.5237)\n",
      "Epoch: [13][110/792]\tTime 1.782 (199.933)\tETA 0:20:15\tLoss 0.5244 (0.5237)\n",
      "Epoch: [13][115/792]\tTime 1.739 (209.025)\tETA 0:19:37\tLoss 0.5243 (0.5237)\n",
      "Epoch: [13][120/792]\tTime 1.760 (218.219)\tETA 0:19:42\tLoss 0.5229 (0.5237)\n",
      "Epoch: [13][125/792]\tTime 1.774 (227.207)\tETA 0:19:43\tLoss 0.5256 (0.5237)\n",
      "Epoch: [13][130/792]\tTime 1.669 (236.143)\tETA 0:18:24\tLoss 0.5261 (0.5237)\n",
      "Epoch: [13][135/792]\tTime 1.785 (245.636)\tETA 0:19:32\tLoss 0.5226 (0.5236)\n",
      "Epoch: [13][140/792]\tTime 1.791 (254.573)\tETA 0:19:27\tLoss 0.5233 (0.5236)\n",
      "Epoch: [13][145/792]\tTime 1.742 (263.836)\tETA 0:18:47\tLoss 0.5224 (0.5235)\n",
      "Epoch: [13][150/792]\tTime 1.726 (272.692)\tETA 0:18:27\tLoss 0.5220 (0.5235)\n",
      "Epoch: [13][155/792]\tTime 1.827 (281.544)\tETA 0:19:23\tLoss 0.5224 (0.5235)\n",
      "Epoch: [13][160/792]\tTime 1.829 (290.610)\tETA 0:19:16\tLoss 0.5232 (0.5235)\n",
      "Epoch: [13][165/792]\tTime 2.044 (300.347)\tETA 0:21:21\tLoss 0.5227 (0.5235)\n",
      "Epoch: [13][170/792]\tTime 2.004 (309.587)\tETA 0:20:46\tLoss 0.5240 (0.5235)\n",
      "Epoch: [13][175/792]\tTime 1.815 (318.768)\tETA 0:18:39\tLoss 0.5229 (0.5235)\n",
      "Epoch: [13][180/792]\tTime 1.788 (327.764)\tETA 0:18:14\tLoss 0.5220 (0.5235)\n",
      "Epoch: [13][185/792]\tTime 1.806 (336.684)\tETA 0:18:16\tLoss 0.5221 (0.5235)\n",
      "Epoch: [13][190/792]\tTime 1.788 (345.522)\tETA 0:17:56\tLoss 0.5252 (0.5235)\n",
      "Epoch: [13][195/792]\tTime 1.798 (354.477)\tETA 0:17:53\tLoss 0.5260 (0.5235)\n",
      "Epoch: [13][200/792]\tTime 1.734 (363.440)\tETA 0:17:06\tLoss 0.5242 (0.5235)\n",
      "Epoch: [13][205/792]\tTime 1.862 (372.704)\tETA 0:18:12\tLoss 0.5229 (0.5234)\n",
      "Epoch: [13][210/792]\tTime 1.841 (381.875)\tETA 0:17:51\tLoss 0.5204 (0.5234)\n",
      "Epoch: [13][215/792]\tTime 1.833 (390.913)\tETA 0:17:37\tLoss 0.5217 (0.5234)\n",
      "Epoch: [13][220/792]\tTime 1.761 (399.739)\tETA 0:16:47\tLoss 0.5213 (0.5234)\n",
      "Epoch: [13][225/792]\tTime 1.850 (409.035)\tETA 0:17:28\tLoss 0.5216 (0.5233)\n",
      "Epoch: [13][230/792]\tTime 1.834 (418.015)\tETA 0:17:10\tLoss 0.5213 (0.5233)\n",
      "Epoch: [13][235/792]\tTime 1.802 (426.940)\tETA 0:16:43\tLoss 0.5238 (0.5232)\n",
      "Epoch: [13][240/792]\tTime 1.801 (435.864)\tETA 0:16:34\tLoss 0.5259 (0.5233)\n",
      "Epoch: [13][245/792]\tTime 1.799 (444.868)\tETA 0:16:23\tLoss 0.5231 (0.5233)\n",
      "Epoch: [13][250/792]\tTime 1.819 (453.851)\tETA 0:16:25\tLoss 0.5271 (0.5233)\n",
      "Epoch: [13][255/792]\tTime 1.886 (462.714)\tETA 0:16:52\tLoss 0.5200 (0.5233)\n",
      "Epoch: [13][260/792]\tTime 1.785 (471.869)\tETA 0:15:49\tLoss 0.5244 (0.5233)\n",
      "Epoch: [13][265/792]\tTime 1.919 (481.163)\tETA 0:16:51\tLoss 0.5215 (0.5233)\n",
      "Epoch: [13][270/792]\tTime 1.796 (490.276)\tETA 0:15:37\tLoss 0.5254 (0.5233)\n",
      "Epoch: [13][275/792]\tTime 1.747 (499.153)\tETA 0:15:03\tLoss 0.5251 (0.5233)\n",
      "Epoch: [13][280/792]\tTime 1.837 (508.128)\tETA 0:15:40\tLoss 0.5212 (0.5233)\n",
      "Epoch: [13][285/792]\tTime 1.802 (517.087)\tETA 0:15:13\tLoss 0.5204 (0.5232)\n",
      "Epoch: [13][290/792]\tTime 1.887 (526.027)\tETA 0:15:47\tLoss 0.5207 (0.5232)\n",
      "Epoch: [13][295/792]\tTime 1.809 (535.228)\tETA 0:14:59\tLoss 0.5204 (0.5232)\n",
      "Epoch: [13][300/792]\tTime 1.787 (544.286)\tETA 0:14:39\tLoss 0.5180 (0.5232)\n",
      "Epoch: [13][305/792]\tTime 1.802 (553.178)\tETA 0:14:37\tLoss 0.5241 (0.5232)\n",
      "Epoch: [13][310/792]\tTime 1.790 (562.202)\tETA 0:14:22\tLoss 0.5233 (0.5232)\n",
      "Epoch: [13][315/792]\tTime 1.824 (571.111)\tETA 0:14:30\tLoss 0.5248 (0.5231)\n",
      "Epoch: [13][320/792]\tTime 1.789 (580.128)\tETA 0:14:04\tLoss 0.5204 (0.5231)\n",
      "Epoch: [13][325/792]\tTime 1.754 (588.909)\tETA 0:13:39\tLoss 0.5267 (0.5231)\n",
      "Epoch: [13][330/792]\tTime 1.759 (598.022)\tETA 0:13:32\tLoss 0.5241 (0.5231)\n",
      "Epoch: [13][335/792]\tTime 1.881 (607.078)\tETA 0:14:19\tLoss 0.5271 (0.5232)\n",
      "Epoch: [13][340/792]\tTime 1.732 (615.921)\tETA 0:13:02\tLoss 0.5208 (0.5231)\n",
      "Epoch: [13][345/792]\tTime 1.743 (624.938)\tETA 0:12:59\tLoss 0.5235 (0.5231)\n",
      "Epoch: [13][350/792]\tTime 1.815 (634.197)\tETA 0:13:22\tLoss 0.5211 (0.5231)\n",
      "Epoch: [13][355/792]\tTime 1.796 (643.290)\tETA 0:13:04\tLoss 0.5214 (0.5231)\n",
      "Epoch: [13][360/792]\tTime 1.782 (652.532)\tETA 0:12:49\tLoss 0.5255 (0.5231)\n",
      "Epoch: [13][365/792]\tTime 1.708 (661.420)\tETA 0:12:09\tLoss 0.5217 (0.5231)\n",
      "Epoch: [13][370/792]\tTime 1.848 (670.582)\tETA 0:12:59\tLoss 0.5229 (0.5231)\n",
      "Epoch: [13][375/792]\tTime 1.772 (679.715)\tETA 0:12:18\tLoss 0.5228 (0.5231)\n",
      "Epoch: [13][380/792]\tTime 1.913 (689.311)\tETA 0:13:08\tLoss 0.5249 (0.5231)\n",
      "Epoch: [13][385/792]\tTime 1.815 (698.375)\tETA 0:12:18\tLoss 0.5204 (0.5231)\n",
      "Epoch: [13][390/792]\tTime 1.773 (707.259)\tETA 0:11:52\tLoss 0.5251 (0.5231)\n",
      "Epoch: [13][395/792]\tTime 1.897 (716.485)\tETA 0:12:33\tLoss 0.5248 (0.5232)\n",
      "Epoch: [13][400/792]\tTime 1.860 (725.411)\tETA 0:12:08\tLoss 0.5234 (0.5232)\n",
      "Epoch: [13][405/792]\tTime 1.762 (734.388)\tETA 0:11:22\tLoss 0.5254 (0.5232)\n",
      "Epoch: [13][410/792]\tTime 1.721 (743.885)\tETA 0:10:57\tLoss 0.5240 (0.5232)\n",
      "Epoch: [13][415/792]\tTime 1.742 (752.950)\tETA 0:10:56\tLoss 0.5244 (0.5232)\n",
      "Epoch: [13][420/792]\tTime 1.804 (761.753)\tETA 0:11:10\tLoss 0.5255 (0.5232)\n",
      "Epoch: [13][425/792]\tTime 1.757 (770.726)\tETA 0:10:44\tLoss 0.5226 (0.5232)\n",
      "Epoch: [13][430/792]\tTime 1.851 (779.723)\tETA 0:11:10\tLoss 0.5229 (0.5232)\n",
      "Epoch: [13][435/792]\tTime 1.907 (788.795)\tETA 0:11:20\tLoss 0.5234 (0.5232)\n",
      "Epoch: [13][440/792]\tTime 1.828 (797.967)\tETA 0:10:43\tLoss 0.5243 (0.5232)\n",
      "Epoch: [13][445/792]\tTime 1.785 (806.942)\tETA 0:10:19\tLoss 0.5243 (0.5232)\n",
      "Epoch: [13][450/792]\tTime 1.891 (816.057)\tETA 0:10:46\tLoss 0.5238 (0.5232)\n",
      "Epoch: [13][455/792]\tTime 1.805 (825.170)\tETA 0:10:08\tLoss 0.5242 (0.5232)\n",
      "Epoch: [13][460/792]\tTime 1.835 (834.196)\tETA 0:10:09\tLoss 0.5225 (0.5232)\n",
      "Epoch: [13][465/792]\tTime 1.816 (843.145)\tETA 0:09:53\tLoss 0.5274 (0.5232)\n",
      "Epoch: [13][470/792]\tTime 1.762 (852.048)\tETA 0:09:27\tLoss 0.5231 (0.5232)\n",
      "Epoch: [13][475/792]\tTime 1.739 (860.912)\tETA 0:09:11\tLoss 0.5244 (0.5233)\n",
      "Epoch: [13][480/792]\tTime 1.806 (869.952)\tETA 0:09:23\tLoss 0.5291 (0.5233)\n",
      "Epoch: [13][485/792]\tTime 1.758 (878.829)\tETA 0:08:59\tLoss 0.5239 (0.5233)\n",
      "Epoch: [13][490/792]\tTime 1.750 (887.647)\tETA 0:08:48\tLoss 0.5252 (0.5233)\n",
      "Epoch: [13][495/792]\tTime 1.765 (896.389)\tETA 0:08:44\tLoss 0.5208 (0.5233)\n",
      "Epoch: [13][500/792]\tTime 1.716 (905.096)\tETA 0:08:21\tLoss 0.5226 (0.5233)\n",
      "Epoch: [13][505/792]\tTime 1.758 (913.934)\tETA 0:08:24\tLoss 0.5197 (0.5233)\n",
      "Epoch: [13][510/792]\tTime 1.781 (922.832)\tETA 0:08:22\tLoss 0.5228 (0.5233)\n",
      "Epoch: [13][515/792]\tTime 1.743 (931.580)\tETA 0:08:02\tLoss 0.5234 (0.5233)\n",
      "Epoch: [13][520/792]\tTime 1.836 (940.557)\tETA 0:08:19\tLoss 0.5238 (0.5233)\n",
      "Epoch: [13][525/792]\tTime 1.828 (949.657)\tETA 0:08:08\tLoss 0.5230 (0.5233)\n",
      "Epoch: [13][530/792]\tTime 1.774 (958.718)\tETA 0:07:44\tLoss 0.5224 (0.5233)\n",
      "Epoch: [13][535/792]\tTime 1.806 (967.672)\tETA 0:07:44\tLoss 0.5228 (0.5233)\n",
      "Epoch: [13][540/792]\tTime 1.763 (976.767)\tETA 0:07:24\tLoss 0.5287 (0.5233)\n",
      "Epoch: [13][545/792]\tTime 1.775 (985.566)\tETA 0:07:18\tLoss 0.5227 (0.5233)\n",
      "Epoch: [13][550/792]\tTime 1.742 (994.522)\tETA 0:07:01\tLoss 0.5223 (0.5233)\n",
      "Epoch: [13][555/792]\tTime 1.752 (1003.415)\tETA 0:06:55\tLoss 0.5270 (0.5233)\n",
      "Epoch: [13][560/792]\tTime 1.773 (1012.375)\tETA 0:06:51\tLoss 0.5210 (0.5233)\n",
      "Epoch: [13][565/792]\tTime 1.879 (1021.381)\tETA 0:07:06\tLoss 0.5207 (0.5233)\n",
      "Epoch: [13][570/792]\tTime 1.880 (1030.665)\tETA 0:06:57\tLoss 0.5217 (0.5233)\n",
      "Epoch: [13][575/792]\tTime 1.762 (1039.629)\tETA 0:06:22\tLoss 0.5230 (0.5233)\n",
      "Epoch: [13][580/792]\tTime 1.792 (1049.118)\tETA 0:06:19\tLoss 0.5221 (0.5233)\n",
      "Epoch: [13][585/792]\tTime 1.788 (1058.094)\tETA 0:06:10\tLoss 0.5209 (0.5233)\n",
      "Epoch: [13][590/792]\tTime 1.723 (1066.896)\tETA 0:05:47\tLoss 0.5229 (0.5233)\n",
      "Epoch: [13][595/792]\tTime 1.763 (1076.037)\tETA 0:05:47\tLoss 0.5227 (0.5233)\n",
      "Epoch: [13][600/792]\tTime 1.879 (1085.425)\tETA 0:06:00\tLoss 0.5234 (0.5233)\n",
      "Epoch: [13][605/792]\tTime 1.827 (1094.520)\tETA 0:05:41\tLoss 0.5212 (0.5233)\n",
      "Epoch: [13][610/792]\tTime 2.347 (1103.938)\tETA 0:07:07\tLoss 0.5252 (0.5233)\n",
      "Epoch: [13][615/792]\tTime 1.970 (1113.240)\tETA 0:05:48\tLoss 0.5226 (0.5233)\n",
      "Epoch: [13][620/792]\tTime 1.751 (1122.321)\tETA 0:05:01\tLoss 0.5207 (0.5233)\n",
      "Epoch: [13][625/792]\tTime 1.826 (1131.320)\tETA 0:05:04\tLoss 0.5188 (0.5233)\n",
      "Epoch: [13][630/792]\tTime 1.823 (1140.268)\tETA 0:04:55\tLoss 0.5220 (0.5232)\n",
      "Epoch: [13][635/792]\tTime 1.776 (1149.211)\tETA 0:04:38\tLoss 0.5237 (0.5232)\n",
      "Epoch: [13][640/792]\tTime 1.831 (1158.153)\tETA 0:04:38\tLoss 0.5243 (0.5232)\n",
      "Epoch: [13][645/792]\tTime 1.746 (1167.093)\tETA 0:04:16\tLoss 0.5238 (0.5232)\n",
      "Epoch: [13][650/792]\tTime 1.738 (1175.821)\tETA 0:04:06\tLoss 0.5230 (0.5232)\n",
      "Epoch: [13][655/792]\tTime 1.780 (1184.603)\tETA 0:04:03\tLoss 0.5220 (0.5232)\n",
      "Epoch: [13][660/792]\tTime 1.742 (1193.497)\tETA 0:03:49\tLoss 0.5217 (0.5232)\n",
      "Epoch: [13][665/792]\tTime 1.828 (1202.455)\tETA 0:03:52\tLoss 0.5232 (0.5232)\n",
      "Epoch: [13][670/792]\tTime 1.726 (1211.367)\tETA 0:03:30\tLoss 0.5228 (0.5232)\n",
      "Epoch: [13][675/792]\tTime 2.295 (1220.723)\tETA 0:04:28\tLoss 0.5219 (0.5232)\n",
      "Epoch: [13][680/792]\tTime 1.797 (1229.682)\tETA 0:03:21\tLoss 0.5213 (0.5232)\n",
      "Epoch: [13][685/792]\tTime 1.774 (1238.909)\tETA 0:03:09\tLoss 0.5246 (0.5232)\n",
      "Epoch: [13][690/792]\tTime 1.813 (1247.780)\tETA 0:03:04\tLoss 0.5239 (0.5232)\n",
      "Epoch: [13][695/792]\tTime 1.788 (1256.733)\tETA 0:02:53\tLoss 0.5232 (0.5232)\n",
      "Epoch: [13][700/792]\tTime 1.772 (1265.866)\tETA 0:02:43\tLoss 0.5231 (0.5232)\n",
      "Epoch: [13][705/792]\tTime 1.733 (1275.085)\tETA 0:02:30\tLoss 0.5199 (0.5232)\n",
      "Epoch: [13][710/792]\tTime 1.800 (1283.973)\tETA 0:02:27\tLoss 0.5230 (0.5232)\n",
      "Epoch: [13][715/792]\tTime 1.773 (1292.796)\tETA 0:02:16\tLoss 0.5216 (0.5232)\n",
      "Epoch: [13][720/792]\tTime 1.794 (1301.691)\tETA 0:02:09\tLoss 0.5209 (0.5231)\n",
      "Epoch: [13][725/792]\tTime 1.709 (1310.521)\tETA 0:01:54\tLoss 0.5205 (0.5231)\n",
      "Epoch: [13][730/792]\tTime 1.746 (1319.607)\tETA 0:01:48\tLoss 0.5224 (0.5231)\n",
      "Epoch: [13][735/792]\tTime 1.831 (1328.734)\tETA 0:01:44\tLoss 0.5273 (0.5231)\n",
      "Epoch: [13][740/792]\tTime 1.778 (1337.630)\tETA 0:01:32\tLoss 0.5235 (0.5231)\n",
      "Epoch: [13][745/792]\tTime 1.843 (1346.748)\tETA 0:01:26\tLoss 0.5253 (0.5231)\n",
      "Epoch: [13][750/792]\tTime 1.743 (1355.698)\tETA 0:01:13\tLoss 0.5232 (0.5231)\n",
      "Epoch: [13][755/792]\tTime 1.804 (1365.158)\tETA 0:01:06\tLoss 0.5220 (0.5231)\n",
      "Epoch: [13][760/792]\tTime 1.768 (1374.075)\tETA 0:00:56\tLoss 0.5239 (0.5231)\n",
      "Epoch: [13][765/792]\tTime 1.928 (1383.286)\tETA 0:00:52\tLoss 0.5268 (0.5231)\n",
      "Epoch: [13][770/792]\tTime 1.869 (1392.492)\tETA 0:00:41\tLoss 0.5215 (0.5231)\n",
      "Epoch: [13][775/792]\tTime 1.816 (1402.295)\tETA 0:00:30\tLoss 0.5261 (0.5231)\n",
      "Epoch: [13][780/792]\tTime 1.831 (1411.965)\tETA 0:00:21\tLoss 0.5224 (0.5231)\n",
      "Epoch: [13][785/792]\tTime 1.832 (1421.111)\tETA 0:00:12\tLoss 0.5227 (0.5231)\n",
      "Epoch: [13][790/792]\tTime 1.775 (1430.069)\tETA 0:00:03\tLoss 0.5247 (0.5231)\n",
      "Epoch: [14][0/792]\tTime 1.900 (1.900)\tETA 0:25:04\tLoss 0.5288 (0.5288)\n",
      "Epoch: [14][5/792]\tTime 1.694 (10.619)\tETA 0:22:13\tLoss 0.5234 (0.5243)\n",
      "Epoch: [14][10/792]\tTime 1.836 (19.858)\tETA 0:23:55\tLoss 0.5267 (0.5246)\n",
      "Epoch: [14][15/792]\tTime 1.865 (28.888)\tETA 0:24:09\tLoss 0.5262 (0.5247)\n",
      "Epoch: [14][20/792]\tTime 1.844 (37.855)\tETA 0:23:43\tLoss 0.5255 (0.5248)\n",
      "Epoch: [14][25/792]\tTime 1.771 (47.019)\tETA 0:22:38\tLoss 0.5276 (0.5245)\n",
      "Epoch: [14][30/792]\tTime 1.768 (56.093)\tETA 0:22:27\tLoss 0.5240 (0.5245)\n",
      "Epoch: [14][35/792]\tTime 1.780 (65.091)\tETA 0:22:27\tLoss 0.5219 (0.5242)\n",
      "Epoch: [14][40/792]\tTime 1.846 (74.360)\tETA 0:23:07\tLoss 0.5209 (0.5239)\n",
      "Epoch: [14][45/792]\tTime 1.868 (83.634)\tETA 0:23:15\tLoss 0.5262 (0.5239)\n",
      "Epoch: [14][50/792]\tTime 1.822 (92.733)\tETA 0:22:31\tLoss 0.5195 (0.5236)\n",
      "Epoch: [14][55/792]\tTime 2.133 (102.144)\tETA 0:26:12\tLoss 0.5222 (0.5236)\n",
      "Epoch: [14][60/792]\tTime 1.827 (111.234)\tETA 0:22:17\tLoss 0.5213 (0.5234)\n",
      "Epoch: [14][65/792]\tTime 1.851 (120.402)\tETA 0:22:25\tLoss 0.5233 (0.5233)\n",
      "Epoch: [14][70/792]\tTime 1.908 (129.602)\tETA 0:22:57\tLoss 0.5253 (0.5233)\n",
      "Epoch: [14][75/792]\tTime 1.858 (138.572)\tETA 0:22:12\tLoss 0.5228 (0.5232)\n",
      "Epoch: [14][80/792]\tTime 1.796 (147.657)\tETA 0:21:18\tLoss 0.5224 (0.5231)\n",
      "Epoch: [14][85/792]\tTime 1.998 (156.879)\tETA 0:23:32\tLoss 0.5235 (0.5230)\n",
      "Epoch: [14][90/792]\tTime 1.783 (165.852)\tETA 0:20:51\tLoss 0.5251 (0.5229)\n",
      "Epoch: [14][95/792]\tTime 1.757 (174.739)\tETA 0:20:24\tLoss 0.5198 (0.5228)\n",
      "Epoch: [14][100/792]\tTime 1.750 (183.657)\tETA 0:20:10\tLoss 0.5202 (0.5228)\n",
      "Epoch: [14][105/792]\tTime 1.680 (192.403)\tETA 0:19:14\tLoss 0.5191 (0.5227)\n",
      "Epoch: [14][110/792]\tTime 1.770 (201.252)\tETA 0:20:06\tLoss 0.5223 (0.5227)\n",
      "Epoch: [14][115/792]\tTime 1.752 (210.216)\tETA 0:19:46\tLoss 0.5233 (0.5226)\n",
      "Epoch: [14][120/792]\tTime 1.869 (219.171)\tETA 0:20:55\tLoss 0.5202 (0.5226)\n",
      "Epoch: [14][125/792]\tTime 1.842 (228.851)\tETA 0:20:28\tLoss 0.5207 (0.5225)\n",
      "Epoch: [14][130/792]\tTime 1.775 (237.907)\tETA 0:19:35\tLoss 0.5214 (0.5225)\n",
      "Epoch: [14][135/792]\tTime 1.753 (246.851)\tETA 0:19:11\tLoss 0.5219 (0.5224)\n",
      "Epoch: [14][140/792]\tTime 1.727 (255.850)\tETA 0:18:45\tLoss 0.5234 (0.5224)\n",
      "Epoch: [14][145/792]\tTime 1.724 (264.637)\tETA 0:18:35\tLoss 0.5243 (0.5224)\n",
      "Epoch: [14][150/792]\tTime 1.803 (273.425)\tETA 0:19:17\tLoss 0.5223 (0.5224)\n",
      "Epoch: [14][155/792]\tTime 1.787 (282.326)\tETA 0:18:58\tLoss 0.5258 (0.5224)\n",
      "Epoch: [14][160/792]\tTime 1.771 (291.207)\tETA 0:18:39\tLoss 0.5215 (0.5224)\n",
      "Epoch: [14][165/792]\tTime 1.800 (299.995)\tETA 0:18:48\tLoss 0.5305 (0.5224)\n",
      "Epoch: [14][170/792]\tTime 1.807 (309.132)\tETA 0:18:43\tLoss 0.5250 (0.5224)\n",
      "Epoch: [14][175/792]\tTime 1.886 (318.420)\tETA 0:19:23\tLoss 0.5245 (0.5224)\n",
      "Epoch: [14][180/792]\tTime 1.800 (327.489)\tETA 0:18:21\tLoss 0.5222 (0.5224)\n",
      "Epoch: [14][185/792]\tTime 1.885 (336.836)\tETA 0:19:04\tLoss 0.5218 (0.5224)\n",
      "Epoch: [14][190/792]\tTime 1.802 (345.877)\tETA 0:18:04\tLoss 0.5264 (0.5224)\n",
      "Epoch: [14][195/792]\tTime 1.794 (355.034)\tETA 0:17:51\tLoss 0.5251 (0.5225)\n",
      "Epoch: [14][200/792]\tTime 1.789 (363.931)\tETA 0:17:38\tLoss 0.5299 (0.5226)\n",
      "Epoch: [14][205/792]\tTime 1.829 (372.843)\tETA 0:17:53\tLoss 0.5217 (0.5226)\n",
      "Epoch: [14][210/792]\tTime 1.756 (381.700)\tETA 0:17:02\tLoss 0.5218 (0.5226)\n",
      "Epoch: [14][215/792]\tTime 1.753 (390.566)\tETA 0:16:51\tLoss 0.5235 (0.5226)\n",
      "Epoch: [14][220/792]\tTime 1.761 (399.518)\tETA 0:16:47\tLoss 0.5218 (0.5226)\n",
      "Epoch: [14][225/792]\tTime 1.736 (408.361)\tETA 0:16:24\tLoss 0.5293 (0.5226)\n",
      "Epoch: [14][230/792]\tTime 1.823 (417.402)\tETA 0:17:04\tLoss 0.5233 (0.5227)\n",
      "Epoch: [14][235/792]\tTime 1.779 (426.397)\tETA 0:16:31\tLoss 0.5232 (0.5227)\n",
      "Epoch: [14][240/792]\tTime 1.877 (435.636)\tETA 0:17:15\tLoss 0.5214 (0.5227)\n",
      "Epoch: [14][245/792]\tTime 1.801 (445.045)\tETA 0:16:25\tLoss 0.5232 (0.5227)\n",
      "Epoch: [14][250/792]\tTime 1.726 (453.957)\tETA 0:15:35\tLoss 0.5221 (0.5227)\n",
      "Epoch: [14][255/792]\tTime 1.787 (463.357)\tETA 0:15:59\tLoss 0.5217 (0.5227)\n",
      "Epoch: [14][260/792]\tTime 1.765 (472.193)\tETA 0:15:39\tLoss 0.5224 (0.5227)\n",
      "Epoch: [14][265/792]\tTime 1.769 (481.096)\tETA 0:15:32\tLoss 0.5221 (0.5227)\n",
      "Epoch: [14][270/792]\tTime 1.848 (490.055)\tETA 0:16:04\tLoss 0.5235 (0.5227)\n",
      "Epoch: [14][275/792]\tTime 1.805 (499.099)\tETA 0:15:32\tLoss 0.5242 (0.5227)\n",
      "Epoch: [14][280/792]\tTime 1.759 (507.970)\tETA 0:15:00\tLoss 0.5226 (0.5227)\n",
      "Epoch: [14][285/792]\tTime 1.788 (516.884)\tETA 0:15:06\tLoss 0.5248 (0.5227)\n",
      "Epoch: [14][290/792]\tTime 1.828 (525.869)\tETA 0:15:17\tLoss 0.5224 (0.5227)\n",
      "Epoch: [14][295/792]\tTime 1.744 (534.651)\tETA 0:14:26\tLoss 0.5257 (0.5227)\n",
      "Epoch: [14][300/792]\tTime 1.803 (544.203)\tETA 0:14:46\tLoss 0.5227 (0.5227)\n",
      "Epoch: [14][305/792]\tTime 2.330 (554.063)\tETA 0:18:54\tLoss 0.5200 (0.5227)\n",
      "Epoch: [14][310/792]\tTime 1.839 (563.561)\tETA 0:14:46\tLoss 0.5222 (0.5227)\n",
      "Epoch: [14][315/792]\tTime 1.852 (572.555)\tETA 0:14:43\tLoss 0.5220 (0.5227)\n",
      "Epoch: [14][320/792]\tTime 1.849 (581.473)\tETA 0:14:32\tLoss 0.5214 (0.5226)\n",
      "Epoch: [14][325/792]\tTime 1.783 (590.763)\tETA 0:13:52\tLoss 0.5222 (0.5227)\n",
      "Epoch: [14][330/792]\tTime 1.836 (599.917)\tETA 0:14:08\tLoss 0.5229 (0.5227)\n",
      "Epoch: [14][335/792]\tTime 1.796 (608.821)\tETA 0:13:40\tLoss 0.5250 (0.5227)\n",
      "Epoch: [14][340/792]\tTime 1.772 (617.881)\tETA 0:13:20\tLoss 0.5221 (0.5227)\n",
      "Epoch: [14][345/792]\tTime 1.762 (626.740)\tETA 0:13:07\tLoss 0.5234 (0.5227)\n",
      "Epoch: [14][350/792]\tTime 1.879 (635.827)\tETA 0:13:50\tLoss 0.5196 (0.5227)\n",
      "Epoch: [14][355/792]\tTime 1.806 (644.751)\tETA 0:13:09\tLoss 0.5272 (0.5227)\n",
      "Epoch: [14][360/792]\tTime 1.771 (653.961)\tETA 0:12:45\tLoss 0.5214 (0.5227)\n",
      "Epoch: [14][365/792]\tTime 1.744 (662.707)\tETA 0:12:24\tLoss 0.5242 (0.5227)\n",
      "Epoch: [14][370/792]\tTime 1.816 (671.572)\tETA 0:12:46\tLoss 0.5228 (0.5227)\n",
      "Epoch: [14][375/792]\tTime 1.808 (680.463)\tETA 0:12:34\tLoss 0.5229 (0.5227)\n",
      "Epoch: [14][380/792]\tTime 1.764 (689.342)\tETA 0:12:06\tLoss 0.5220 (0.5226)\n",
      "Epoch: [14][385/792]\tTime 1.790 (698.240)\tETA 0:12:08\tLoss 0.5207 (0.5226)\n",
      "Epoch: [14][390/792]\tTime 1.801 (707.295)\tETA 0:12:04\tLoss 0.5218 (0.5226)\n",
      "Epoch: [14][395/792]\tTime 1.816 (716.562)\tETA 0:12:00\tLoss 0.5227 (0.5226)\n",
      "Epoch: [14][400/792]\tTime 1.770 (725.477)\tETA 0:11:34\tLoss 0.5195 (0.5226)\n",
      "Epoch: [14][405/792]\tTime 1.834 (734.604)\tETA 0:11:49\tLoss 0.5216 (0.5226)\n",
      "Epoch: [14][410/792]\tTime 1.798 (743.589)\tETA 0:11:26\tLoss 0.5206 (0.5226)\n",
      "Epoch: [14][415/792]\tTime 1.844 (752.450)\tETA 0:11:35\tLoss 0.5239 (0.5226)\n",
      "Epoch: [14][420/792]\tTime 1.850 (761.818)\tETA 0:11:28\tLoss 0.5206 (0.5226)\n",
      "Epoch: [14][425/792]\tTime 1.841 (771.176)\tETA 0:11:15\tLoss 0.5264 (0.5226)\n",
      "Epoch: [14][430/792]\tTime 1.800 (780.164)\tETA 0:10:51\tLoss 0.5221 (0.5226)\n",
      "Epoch: [14][435/792]\tTime 2.008 (789.348)\tETA 0:11:56\tLoss 0.5238 (0.5226)\n",
      "Epoch: [14][440/792]\tTime 1.878 (798.488)\tETA 0:11:01\tLoss 0.5216 (0.5226)\n",
      "Epoch: [14][445/792]\tTime 1.772 (807.419)\tETA 0:10:14\tLoss 0.5215 (0.5226)\n",
      "Epoch: [14][450/792]\tTime 1.704 (816.338)\tETA 0:09:42\tLoss 0.5213 (0.5226)\n",
      "Epoch: [14][455/792]\tTime 1.719 (825.102)\tETA 0:09:39\tLoss 0.5219 (0.5226)\n",
      "Epoch: [14][460/792]\tTime 1.794 (834.324)\tETA 0:09:55\tLoss 0.5280 (0.5226)\n",
      "Epoch: [14][465/792]\tTime 1.739 (843.431)\tETA 0:09:28\tLoss 0.5210 (0.5226)\n",
      "Epoch: [14][470/792]\tTime 1.969 (852.674)\tETA 0:10:34\tLoss 0.5228 (0.5226)\n",
      "Epoch: [14][475/792]\tTime 1.872 (862.011)\tETA 0:09:53\tLoss 0.5276 (0.5226)\n",
      "Epoch: [14][480/792]\tTime 1.774 (871.290)\tETA 0:09:13\tLoss 0.5240 (0.5226)\n",
      "Epoch: [14][485/792]\tTime 1.785 (880.355)\tETA 0:09:08\tLoss 0.5249 (0.5226)\n",
      "Epoch: [14][490/792]\tTime 1.769 (889.265)\tETA 0:08:54\tLoss 0.5262 (0.5226)\n",
      "Epoch: [14][495/792]\tTime 1.816 (898.216)\tETA 0:08:59\tLoss 0.5226 (0.5226)\n",
      "Epoch: [14][500/792]\tTime 1.810 (907.330)\tETA 0:08:48\tLoss 0.5203 (0.5226)\n",
      "Epoch: [14][505/792]\tTime 1.794 (916.192)\tETA 0:08:34\tLoss 0.5233 (0.5226)\n",
      "Epoch: [14][510/792]\tTime 1.807 (925.368)\tETA 0:08:29\tLoss 0.5242 (0.5227)\n",
      "Epoch: [14][515/792]\tTime 1.770 (934.537)\tETA 0:08:10\tLoss 0.5226 (0.5227)\n",
      "Epoch: [14][520/792]\tTime 1.724 (943.548)\tETA 0:07:49\tLoss 0.5264 (0.5227)\n",
      "Epoch: [14][525/792]\tTime 1.846 (952.681)\tETA 0:08:12\tLoss 0.5270 (0.5227)\n",
      "Epoch: [14][530/792]\tTime 1.825 (961.578)\tETA 0:07:58\tLoss 0.5220 (0.5227)\n",
      "Epoch: [14][535/792]\tTime 1.829 (970.539)\tETA 0:07:50\tLoss 0.5210 (0.5227)\n",
      "Epoch: [14][540/792]\tTime 1.818 (979.464)\tETA 0:07:38\tLoss 0.5208 (0.5227)\n",
      "Epoch: [14][545/792]\tTime 1.725 (988.208)\tETA 0:07:06\tLoss 0.5216 (0.5227)\n",
      "Epoch: [14][550/792]\tTime 1.707 (996.938)\tETA 0:06:53\tLoss 0.5236 (0.5227)\n",
      "Epoch: [14][555/792]\tTime 1.720 (1005.624)\tETA 0:06:47\tLoss 0.5213 (0.5227)\n",
      "Epoch: [14][560/792]\tTime 1.767 (1014.551)\tETA 0:06:49\tLoss 0.5244 (0.5227)\n",
      "Epoch: [14][565/792]\tTime 1.794 (1023.578)\tETA 0:06:47\tLoss 0.5198 (0.5227)\n",
      "Epoch: [14][570/792]\tTime 1.878 (1032.488)\tETA 0:06:56\tLoss 0.5232 (0.5227)\n",
      "Epoch: [14][575/792]\tTime 1.811 (1041.334)\tETA 0:06:32\tLoss 0.5217 (0.5227)\n",
      "Epoch: [14][580/792]\tTime 2.169 (1051.107)\tETA 0:07:39\tLoss 0.5209 (0.5227)\n",
      "Epoch: [14][585/792]\tTime 1.800 (1061.081)\tETA 0:06:12\tLoss 0.5253 (0.5227)\n",
      "Epoch: [14][590/792]\tTime 1.774 (1070.158)\tETA 0:05:58\tLoss 0.5207 (0.5227)\n",
      "Epoch: [14][595/792]\tTime 1.786 (1079.246)\tETA 0:05:51\tLoss 0.5232 (0.5227)\n",
      "Epoch: [14][600/792]\tTime 1.785 (1088.101)\tETA 0:05:42\tLoss 0.5221 (0.5227)\n",
      "Epoch: [14][605/792]\tTime 1.783 (1097.282)\tETA 0:05:33\tLoss 0.5213 (0.5227)\n",
      "Epoch: [14][610/792]\tTime 1.759 (1106.397)\tETA 0:05:20\tLoss 0.5218 (0.5227)\n",
      "Epoch: [14][615/792]\tTime 1.879 (1115.645)\tETA 0:05:32\tLoss 0.5210 (0.5227)\n",
      "Epoch: [14][620/792]\tTime 1.838 (1124.740)\tETA 0:05:16\tLoss 0.5211 (0.5226)\n",
      "Epoch: [14][625/792]\tTime 1.756 (1133.821)\tETA 0:04:53\tLoss 0.5232 (0.5226)\n",
      "Epoch: [14][630/792]\tTime 1.826 (1142.705)\tETA 0:04:55\tLoss 0.5215 (0.5226)\n",
      "Epoch: [14][635/792]\tTime 1.742 (1151.650)\tETA 0:04:33\tLoss 0.5214 (0.5226)\n",
      "Epoch: [14][640/792]\tTime 1.785 (1160.559)\tETA 0:04:31\tLoss 0.5206 (0.5226)\n",
      "Epoch: [14][645/792]\tTime 1.742 (1169.417)\tETA 0:04:16\tLoss 0.5251 (0.5226)\n",
      "Epoch: [14][650/792]\tTime 1.819 (1178.314)\tETA 0:04:18\tLoss 0.5226 (0.5226)\n",
      "Epoch: [14][655/792]\tTime 1.787 (1187.523)\tETA 0:04:04\tLoss 0.5219 (0.5226)\n",
      "Epoch: [14][660/792]\tTime 1.762 (1196.454)\tETA 0:03:52\tLoss 0.5269 (0.5226)\n",
      "Epoch: [14][665/792]\tTime 1.844 (1205.565)\tETA 0:03:54\tLoss 0.5231 (0.5226)\n",
      "Epoch: [14][670/792]\tTime 1.768 (1214.380)\tETA 0:03:35\tLoss 0.5200 (0.5226)\n",
      "Epoch: [14][675/792]\tTime 1.741 (1223.395)\tETA 0:03:23\tLoss 0.5227 (0.5226)\n",
      "Epoch: [14][680/792]\tTime 1.785 (1232.408)\tETA 0:03:19\tLoss 0.5229 (0.5226)\n",
      "Epoch: [14][685/792]\tTime 1.779 (1241.169)\tETA 0:03:10\tLoss 0.5196 (0.5226)\n",
      "Epoch: [14][690/792]\tTime 1.751 (1249.997)\tETA 0:02:58\tLoss 0.5215 (0.5226)\n",
      "Epoch: [14][695/792]\tTime 1.712 (1258.705)\tETA 0:02:46\tLoss 0.5247 (0.5226)\n",
      "Epoch: [14][700/792]\tTime 1.695 (1267.468)\tETA 0:02:35\tLoss 0.5270 (0.5226)\n",
      "Epoch: [14][705/792]\tTime 1.735 (1276.218)\tETA 0:02:30\tLoss 0.5204 (0.5226)\n",
      "Epoch: [14][710/792]\tTime 1.730 (1285.438)\tETA 0:02:21\tLoss 0.5211 (0.5226)\n",
      "Epoch: [14][715/792]\tTime 1.741 (1294.311)\tETA 0:02:14\tLoss 0.5204 (0.5226)\n",
      "Epoch: [14][720/792]\tTime 1.811 (1303.468)\tETA 0:02:10\tLoss 0.5210 (0.5226)\n",
      "Epoch: [14][725/792]\tTime 1.786 (1312.551)\tETA 0:01:59\tLoss 0.5262 (0.5226)\n",
      "Epoch: [14][730/792]\tTime 1.797 (1321.458)\tETA 0:01:51\tLoss 0.5245 (0.5226)\n",
      "Epoch: [14][735/792]\tTime 1.777 (1330.486)\tETA 0:01:41\tLoss 0.5235 (0.5226)\n",
      "Epoch: [14][740/792]\tTime 1.751 (1339.396)\tETA 0:01:31\tLoss 0.5205 (0.5226)\n",
      "Epoch: [14][745/792]\tTime 1.727 (1348.358)\tETA 0:01:21\tLoss 0.5204 (0.5226)\n",
      "Epoch: [14][750/792]\tTime 1.866 (1357.595)\tETA 0:01:18\tLoss 0.5242 (0.5226)\n",
      "Epoch: [14][755/792]\tTime 1.785 (1366.471)\tETA 0:01:06\tLoss 0.5199 (0.5226)\n",
      "Epoch: [14][760/792]\tTime 1.770 (1375.386)\tETA 0:00:56\tLoss 0.5227 (0.5226)\n",
      "Epoch: [14][765/792]\tTime 1.780 (1384.313)\tETA 0:00:48\tLoss 0.5215 (0.5226)\n",
      "Epoch: [14][770/792]\tTime 1.862 (1393.352)\tETA 0:00:40\tLoss 0.5239 (0.5226)\n",
      "Epoch: [14][775/792]\tTime 1.762 (1402.081)\tETA 0:00:29\tLoss 0.5232 (0.5226)\n",
      "Epoch: [14][780/792]\tTime 1.766 (1410.873)\tETA 0:00:21\tLoss 0.5240 (0.5226)\n",
      "Epoch: [14][785/792]\tTime 1.763 (1419.712)\tETA 0:00:12\tLoss 0.5213 (0.5226)\n",
      "Epoch: [14][790/792]\tTime 1.779 (1428.646)\tETA 0:00:03\tLoss 0.5241 (0.5226)\n",
      "Epoch: [15][0/792]\tTime 1.724 (1.724)\tETA 0:22:45\tLoss 0.5219 (0.5219)\n",
      "Epoch: [15][5/792]\tTime 1.756 (10.556)\tETA 0:23:02\tLoss 0.5205 (0.5223)\n",
      "Epoch: [15][10/792]\tTime 1.779 (19.490)\tETA 0:23:11\tLoss 0.5217 (0.5218)\n",
      "Epoch: [15][15/792]\tTime 1.810 (28.359)\tETA 0:23:26\tLoss 0.5217 (0.5218)\n",
      "Epoch: [15][20/792]\tTime 1.737 (37.199)\tETA 0:22:21\tLoss 0.5212 (0.5217)\n",
      "Epoch: [15][25/792]\tTime 1.753 (46.033)\tETA 0:22:24\tLoss 0.5221 (0.5214)\n",
      "Epoch: [15][30/792]\tTime 1.786 (55.050)\tETA 0:22:40\tLoss 0.5225 (0.5214)\n",
      "Epoch: [15][35/792]\tTime 1.794 (64.032)\tETA 0:22:37\tLoss 0.5204 (0.5213)\n",
      "Epoch: [15][40/792]\tTime 1.793 (72.887)\tETA 0:22:28\tLoss 0.5180 (0.5212)\n",
      "Epoch: [15][45/792]\tTime 1.827 (81.841)\tETA 0:22:44\tLoss 0.5213 (0.5212)\n",
      "Epoch: [15][50/792]\tTime 1.807 (90.806)\tETA 0:22:21\tLoss 0.5218 (0.5213)\n",
      "Epoch: [15][55/792]\tTime 1.751 (99.726)\tETA 0:21:30\tLoss 0.5190 (0.5212)\n",
      "Epoch: [15][60/792]\tTime 1.872 (108.824)\tETA 0:22:50\tLoss 0.5217 (0.5212)\n",
      "Epoch: [15][65/792]\tTime 1.872 (117.781)\tETA 0:22:40\tLoss 0.5259 (0.5213)\n",
      "Epoch: [15][70/792]\tTime 1.808 (126.665)\tETA 0:21:45\tLoss 0.5200 (0.5214)\n",
      "Epoch: [15][75/792]\tTime 1.779 (135.716)\tETA 0:21:15\tLoss 0.5247 (0.5216)\n",
      "Epoch: [15][80/792]\tTime 1.869 (144.689)\tETA 0:22:10\tLoss 0.5276 (0.5219)\n",
      "Epoch: [15][85/792]\tTime 1.890 (153.807)\tETA 0:22:16\tLoss 0.5248 (0.5220)\n",
      "Epoch: [15][90/792]\tTime 1.829 (163.017)\tETA 0:21:23\tLoss 0.5212 (0.5221)\n",
      "Epoch: [15][95/792]\tTime 1.821 (172.094)\tETA 0:21:09\tLoss 0.5212 (0.5221)\n",
      "Epoch: [15][100/792]\tTime 1.788 (181.215)\tETA 0:20:37\tLoss 0.5244 (0.5222)\n",
      "Epoch: [15][105/792]\tTime 1.838 (190.343)\tETA 0:21:02\tLoss 0.5229 (0.5223)\n",
      "Epoch: [15][110/792]\tTime 1.772 (199.648)\tETA 0:20:08\tLoss 0.5215 (0.5225)\n",
      "Epoch: [15][115/792]\tTime 1.787 (209.023)\tETA 0:20:09\tLoss 0.5241 (0.5225)\n",
      "Epoch: [15][120/792]\tTime 2.102 (218.383)\tETA 0:23:32\tLoss 0.5282 (0.5227)\n",
      "Epoch: [15][125/792]\tTime 1.727 (227.431)\tETA 0:19:12\tLoss 0.5214 (0.5227)\n",
      "Epoch: [15][130/792]\tTime 1.794 (236.322)\tETA 0:19:47\tLoss 0.5209 (0.5226)\n",
      "Epoch: [15][135/792]\tTime 1.715 (245.579)\tETA 0:18:46\tLoss 0.5209 (0.5226)\n",
      "Epoch: [15][140/792]\tTime 1.710 (254.530)\tETA 0:18:34\tLoss 0.5252 (0.5226)\n",
      "Epoch: [15][145/792]\tTime 1.755 (263.450)\tETA 0:18:55\tLoss 0.5224 (0.5225)\n",
      "Epoch: [15][150/792]\tTime 1.797 (272.277)\tETA 0:19:13\tLoss 0.5217 (0.5225)\n",
      "Epoch: [15][155/792]\tTime 1.725 (281.434)\tETA 0:18:18\tLoss 0.5244 (0.5225)\n",
      "Epoch: [15][160/792]\tTime 1.773 (290.193)\tETA 0:18:40\tLoss 0.5245 (0.5225)\n",
      "Epoch: [15][165/792]\tTime 1.790 (299.020)\tETA 0:18:42\tLoss 0.5216 (0.5225)\n",
      "Epoch: [15][170/792]\tTime 1.792 (307.829)\tETA 0:18:34\tLoss 0.5206 (0.5225)\n",
      "Epoch: [15][175/792]\tTime 1.725 (316.695)\tETA 0:17:44\tLoss 0.5202 (0.5224)\n",
      "Epoch: [15][180/792]\tTime 1.780 (325.784)\tETA 0:18:09\tLoss 0.5212 (0.5224)\n",
      "Epoch: [15][185/792]\tTime 1.788 (334.665)\tETA 0:18:05\tLoss 0.5208 (0.5224)\n",
      "Epoch: [15][190/792]\tTime 1.799 (343.731)\tETA 0:18:03\tLoss 0.5225 (0.5224)\n",
      "Epoch: [15][195/792]\tTime 1.812 (352.835)\tETA 0:18:01\tLoss 0.5215 (0.5224)\n",
      "Epoch: [15][200/792]\tTime 1.790 (362.249)\tETA 0:17:39\tLoss 0.5212 (0.5224)\n",
      "Epoch: [15][205/792]\tTime 1.849 (371.341)\tETA 0:18:05\tLoss 0.5212 (0.5224)\n",
      "Epoch: [15][210/792]\tTime 1.763 (380.272)\tETA 0:17:06\tLoss 0.5242 (0.5224)\n",
      "Epoch: [15][215/792]\tTime 1.783 (389.336)\tETA 0:17:08\tLoss 0.5227 (0.5224)\n",
      "Epoch: [15][220/792]\tTime 1.737 (398.110)\tETA 0:16:33\tLoss 0.5198 (0.5224)\n",
      "Epoch: [15][225/792]\tTime 1.721 (406.921)\tETA 0:16:15\tLoss 0.5219 (0.5224)\n",
      "Epoch: [15][230/792]\tTime 1.838 (415.902)\tETA 0:17:13\tLoss 0.5216 (0.5224)\n",
      "Epoch: [15][235/792]\tTime 1.790 (424.983)\tETA 0:16:37\tLoss 0.5239 (0.5224)\n",
      "Epoch: [15][240/792]\tTime 1.806 (433.875)\tETA 0:16:37\tLoss 0.5231 (0.5224)\n",
      "Epoch: [15][245/792]\tTime 1.763 (442.963)\tETA 0:16:04\tLoss 0.5203 (0.5223)\n",
      "Epoch: [15][250/792]\tTime 1.770 (451.870)\tETA 0:15:59\tLoss 0.5226 (0.5223)\n",
      "Epoch: [15][255/792]\tTime 1.795 (460.945)\tETA 0:16:03\tLoss 0.5228 (0.5223)\n",
      "Epoch: [15][260/792]\tTime 1.756 (470.046)\tETA 0:15:34\tLoss 0.5221 (0.5223)\n",
      "Epoch: [15][265/792]\tTime 1.859 (479.174)\tETA 0:16:19\tLoss 0.5235 (0.5223)\n",
      "Epoch: [15][270/792]\tTime 1.831 (488.222)\tETA 0:15:55\tLoss 0.5244 (0.5223)\n",
      "Epoch: [15][275/792]\tTime 1.947 (497.407)\tETA 0:16:46\tLoss 0.5212 (0.5223)\n",
      "Epoch: [15][280/792]\tTime 1.801 (506.466)\tETA 0:15:22\tLoss 0.5232 (0.5223)\n",
      "Epoch: [15][285/792]\tTime 1.825 (515.526)\tETA 0:15:25\tLoss 0.5225 (0.5224)\n",
      "Epoch: [15][290/792]\tTime 1.781 (524.439)\tETA 0:14:54\tLoss 0.5219 (0.5224)\n",
      "Epoch: [15][295/792]\tTime 1.767 (533.553)\tETA 0:14:38\tLoss 0.5195 (0.5224)\n",
      "Epoch: [15][300/792]\tTime 1.687 (542.366)\tETA 0:13:49\tLoss 0.5217 (0.5223)\n",
      "Epoch: [15][305/792]\tTime 1.768 (551.304)\tETA 0:14:21\tLoss 0.5222 (0.5223)\n",
      "Epoch: [15][310/792]\tTime 1.781 (560.244)\tETA 0:14:18\tLoss 0.5235 (0.5223)\n",
      "Epoch: [15][315/792]\tTime 1.772 (569.300)\tETA 0:14:05\tLoss 0.5227 (0.5224)\n",
      "Epoch: [15][320/792]\tTime 1.756 (578.161)\tETA 0:13:48\tLoss 0.5227 (0.5224)\n",
      "Epoch: [15][325/792]\tTime 1.782 (587.169)\tETA 0:13:52\tLoss 0.5221 (0.5224)\n",
      "Epoch: [15][330/792]\tTime 1.990 (596.555)\tETA 0:15:19\tLoss 0.5191 (0.5224)\n",
      "Epoch: [15][335/792]\tTime 1.829 (605.611)\tETA 0:13:55\tLoss 0.5219 (0.5224)\n",
      "Epoch: [15][340/792]\tTime 1.855 (614.663)\tETA 0:13:58\tLoss 0.5287 (0.5225)\n",
      "Epoch: [15][345/792]\tTime 1.813 (623.631)\tETA 0:13:30\tLoss 0.5284 (0.5225)\n",
      "Epoch: [15][350/792]\tTime 1.839 (632.554)\tETA 0:13:32\tLoss 0.5261 (0.5226)\n",
      "Epoch: [15][355/792]\tTime 1.793 (641.623)\tETA 0:13:03\tLoss 0.5224 (0.5226)\n",
      "Epoch: [15][360/792]\tTime 1.802 (650.672)\tETA 0:12:58\tLoss 0.5228 (0.5226)\n",
      "Epoch: [15][365/792]\tTime 1.782 (659.525)\tETA 0:12:40\tLoss 0.5201 (0.5226)\n",
      "Epoch: [15][370/792]\tTime 1.727 (668.332)\tETA 0:12:08\tLoss 0.5211 (0.5225)\n",
      "Epoch: [15][375/792]\tTime 1.744 (677.326)\tETA 0:12:07\tLoss 0.5250 (0.5225)\n",
      "Epoch: [15][380/792]\tTime 1.758 (686.272)\tETA 0:12:04\tLoss 0.5234 (0.5225)\n",
      "Epoch: [15][385/792]\tTime 1.760 (695.356)\tETA 0:11:56\tLoss 0.5215 (0.5225)\n",
      "Epoch: [15][390/792]\tTime 1.732 (704.330)\tETA 0:11:36\tLoss 0.5226 (0.5225)\n",
      "Epoch: [15][395/792]\tTime 1.759 (713.196)\tETA 0:11:38\tLoss 0.5223 (0.5225)\n",
      "Epoch: [15][400/792]\tTime 1.867 (722.403)\tETA 0:12:11\tLoss 0.5229 (0.5225)\n",
      "Epoch: [15][405/792]\tTime 1.754 (731.271)\tETA 0:11:18\tLoss 0.5219 (0.5225)\n",
      "Epoch: [15][410/792]\tTime 1.833 (740.343)\tETA 0:11:40\tLoss 0.5195 (0.5225)\n",
      "Epoch: [15][415/792]\tTime 1.734 (749.442)\tETA 0:10:53\tLoss 0.5204 (0.5225)\n",
      "Epoch: [15][420/792]\tTime 1.769 (758.282)\tETA 0:10:58\tLoss 0.5212 (0.5224)\n",
      "Epoch: [15][425/792]\tTime 1.772 (767.165)\tETA 0:10:50\tLoss 0.5211 (0.5224)\n",
      "Epoch: [15][430/792]\tTime 1.773 (776.064)\tETA 0:10:41\tLoss 0.5195 (0.5224)\n",
      "Epoch: [15][435/792]\tTime 1.744 (785.117)\tETA 0:10:22\tLoss 0.5219 (0.5224)\n",
      "Epoch: [15][440/792]\tTime 1.802 (793.974)\tETA 0:10:34\tLoss 0.5203 (0.5224)\n",
      "Epoch: [15][445/792]\tTime 1.795 (802.910)\tETA 0:10:22\tLoss 0.5214 (0.5224)\n",
      "Epoch: [15][450/792]\tTime 1.721 (811.739)\tETA 0:09:48\tLoss 0.5210 (0.5224)\n",
      "Epoch: [15][455/792]\tTime 1.735 (820.566)\tETA 0:09:44\tLoss 0.5207 (0.5224)\n",
      "Epoch: [15][460/792]\tTime 1.721 (829.370)\tETA 0:09:31\tLoss 0.5221 (0.5223)\n",
      "Epoch: [15][465/792]\tTime 1.845 (838.324)\tETA 0:10:03\tLoss 0.5216 (0.5223)\n",
      "Epoch: [15][470/792]\tTime 1.789 (847.256)\tETA 0:09:35\tLoss 0.5211 (0.5223)\n",
      "Epoch: [15][475/792]\tTime 1.843 (856.498)\tETA 0:09:44\tLoss 0.5204 (0.5223)\n",
      "Epoch: [15][480/792]\tTime 1.881 (865.740)\tETA 0:09:46\tLoss 0.5222 (0.5223)\n",
      "Epoch: [15][485/792]\tTime 1.835 (874.677)\tETA 0:09:23\tLoss 0.5208 (0.5223)\n",
      "Epoch: [15][490/792]\tTime 1.758 (883.550)\tETA 0:08:50\tLoss 0.5206 (0.5223)\n",
      "Epoch: [15][495/792]\tTime 2.193 (892.889)\tETA 0:10:51\tLoss 0.5202 (0.5223)\n",
      "Epoch: [15][500/792]\tTime 1.818 (901.953)\tETA 0:08:50\tLoss 0.5216 (0.5223)\n",
      "Epoch: [15][505/792]\tTime 1.860 (910.816)\tETA 0:08:53\tLoss 0.5225 (0.5223)\n",
      "Epoch: [15][510/792]\tTime 1.783 (919.763)\tETA 0:08:22\tLoss 0.5245 (0.5223)\n",
      "Epoch: [15][515/792]\tTime 1.765 (928.584)\tETA 0:08:08\tLoss 0.5224 (0.5223)\n",
      "Epoch: [15][520/792]\tTime 1.796 (937.536)\tETA 0:08:08\tLoss 0.5259 (0.5223)\n",
      "Epoch: [15][525/792]\tTime 1.751 (946.386)\tETA 0:07:47\tLoss 0.5256 (0.5223)\n",
      "Epoch: [15][530/792]\tTime 1.800 (955.262)\tETA 0:07:51\tLoss 0.5197 (0.5223)\n",
      "Epoch: [15][535/792]\tTime 1.806 (964.119)\tETA 0:07:44\tLoss 0.5230 (0.5223)\n",
      "Epoch: [15][540/792]\tTime 1.846 (973.112)\tETA 0:07:45\tLoss 0.5181 (0.5223)\n",
      "Epoch: [15][545/792]\tTime 1.739 (982.049)\tETA 0:07:09\tLoss 0.5225 (0.5223)\n",
      "Epoch: [15][550/792]\tTime 1.890 (991.023)\tETA 0:07:37\tLoss 0.5200 (0.5223)\n",
      "Epoch: [15][555/792]\tTime 1.758 (999.949)\tETA 0:06:56\tLoss 0.5196 (0.5223)\n",
      "Epoch: [15][560/792]\tTime 1.793 (1008.896)\tETA 0:06:55\tLoss 0.5196 (0.5223)\n",
      "Epoch: [15][565/792]\tTime 1.787 (1018.088)\tETA 0:06:45\tLoss 0.5218 (0.5223)\n",
      "Epoch: [15][570/792]\tTime 1.791 (1027.121)\tETA 0:06:37\tLoss 0.5192 (0.5223)\n",
      "Epoch: [15][575/792]\tTime 1.745 (1035.940)\tETA 0:06:18\tLoss 0.5229 (0.5223)\n",
      "Epoch: [15][580/792]\tTime 1.862 (1044.802)\tETA 0:06:34\tLoss 0.5217 (0.5223)\n",
      "Epoch: [15][585/792]\tTime 1.802 (1053.719)\tETA 0:06:13\tLoss 0.5229 (0.5223)\n",
      "Epoch: [15][590/792]\tTime 1.750 (1062.755)\tETA 0:05:53\tLoss 0.5211 (0.5223)\n",
      "Epoch: [15][595/792]\tTime 1.838 (1071.769)\tETA 0:06:02\tLoss 0.5243 (0.5223)\n",
      "Epoch: [15][600/792]\tTime 1.803 (1080.699)\tETA 0:05:46\tLoss 0.5183 (0.5223)\n",
      "Epoch: [15][605/792]\tTime 1.885 (1089.814)\tETA 0:05:52\tLoss 0.5222 (0.5223)\n",
      "Epoch: [15][610/792]\tTime 1.782 (1099.106)\tETA 0:05:24\tLoss 0.5213 (0.5223)\n",
      "Epoch: [15][615/792]\tTime 1.815 (1108.368)\tETA 0:05:21\tLoss 0.5246 (0.5223)\n",
      "Epoch: [15][620/792]\tTime 2.297 (1117.860)\tETA 0:06:35\tLoss 0.5229 (0.5223)\n",
      "Epoch: [15][625/792]\tTime 1.794 (1126.747)\tETA 0:04:59\tLoss 0.5211 (0.5223)\n",
      "Epoch: [15][630/792]\tTime 1.754 (1135.625)\tETA 0:04:44\tLoss 0.5193 (0.5222)\n",
      "Epoch: [15][635/792]\tTime 1.749 (1144.581)\tETA 0:04:34\tLoss 0.5215 (0.5222)\n",
      "Epoch: [15][640/792]\tTime 1.801 (1153.622)\tETA 0:04:33\tLoss 0.5215 (0.5223)\n",
      "Epoch: [15][645/792]\tTime 1.773 (1162.600)\tETA 0:04:20\tLoss 0.5192 (0.5222)\n",
      "Epoch: [15][650/792]\tTime 1.753 (1171.628)\tETA 0:04:08\tLoss 0.5229 (0.5222)\n",
      "Epoch: [15][655/792]\tTime 1.835 (1180.749)\tETA 0:04:11\tLoss 0.5203 (0.5222)\n",
      "Epoch: [15][660/792]\tTime 1.759 (1189.803)\tETA 0:03:52\tLoss 0.5229 (0.5222)\n",
      "Epoch: [15][665/792]\tTime 1.833 (1198.892)\tETA 0:03:52\tLoss 0.5232 (0.5222)\n",
      "Epoch: [15][670/792]\tTime 1.725 (1207.855)\tETA 0:03:30\tLoss 0.5184 (0.5222)\n",
      "Epoch: [15][675/792]\tTime 1.801 (1217.026)\tETA 0:03:30\tLoss 0.5192 (0.5222)\n",
      "Epoch: [15][680/792]\tTime 1.852 (1226.040)\tETA 0:03:27\tLoss 0.5236 (0.5222)\n",
      "Epoch: [15][685/792]\tTime 1.748 (1234.908)\tETA 0:03:07\tLoss 0.5212 (0.5222)\n",
      "Epoch: [15][690/792]\tTime 1.896 (1244.440)\tETA 0:03:13\tLoss 0.5203 (0.5222)\n",
      "Epoch: [15][695/792]\tTime 1.877 (1253.537)\tETA 0:03:02\tLoss 0.5214 (0.5222)\n",
      "Epoch: [15][700/792]\tTime 1.722 (1262.843)\tETA 0:02:38\tLoss 0.5227 (0.5222)\n",
      "Epoch: [15][705/792]\tTime 1.790 (1271.782)\tETA 0:02:35\tLoss 0.5251 (0.5222)\n",
      "Epoch: [15][710/792]\tTime 1.783 (1280.781)\tETA 0:02:26\tLoss 0.5191 (0.5222)\n",
      "Epoch: [15][715/792]\tTime 1.805 (1290.022)\tETA 0:02:19\tLoss 0.5214 (0.5222)\n",
      "Epoch: [15][720/792]\tTime 1.780 (1299.098)\tETA 0:02:08\tLoss 0.5216 (0.5222)\n",
      "Epoch: [15][725/792]\tTime 1.767 (1308.410)\tETA 0:01:58\tLoss 0.5200 (0.5222)\n",
      "Epoch: [15][730/792]\tTime 1.763 (1317.364)\tETA 0:01:49\tLoss 0.5237 (0.5222)\n",
      "Epoch: [15][735/792]\tTime 1.859 (1326.526)\tETA 0:01:45\tLoss 0.5201 (0.5222)\n",
      "Epoch: [15][740/792]\tTime 1.784 (1335.864)\tETA 0:01:32\tLoss 0.5192 (0.5222)\n",
      "Epoch: [15][745/792]\tTime 1.849 (1344.825)\tETA 0:01:26\tLoss 0.5205 (0.5221)\n",
      "Epoch: [15][750/792]\tTime 1.746 (1353.614)\tETA 0:01:13\tLoss 0.5218 (0.5221)\n",
      "Epoch: [15][755/792]\tTime 1.855 (1362.799)\tETA 0:01:08\tLoss 0.5197 (0.5221)\n",
      "Epoch: [15][760/792]\tTime 1.773 (1371.696)\tETA 0:00:56\tLoss 0.5203 (0.5221)\n",
      "Epoch: [15][765/792]\tTime 1.864 (1380.822)\tETA 0:00:50\tLoss 0.5231 (0.5221)\n",
      "Epoch: [15][770/792]\tTime 1.813 (1389.696)\tETA 0:00:39\tLoss 0.5225 (0.5221)\n",
      "Epoch: [15][775/792]\tTime 1.882 (1398.787)\tETA 0:00:31\tLoss 0.5222 (0.5221)\n",
      "Epoch: [15][780/792]\tTime 1.798 (1407.659)\tETA 0:00:21\tLoss 0.5224 (0.5221)\n",
      "Epoch: [15][785/792]\tTime 1.743 (1416.587)\tETA 0:00:12\tLoss 0.5200 (0.5221)\n",
      "Epoch: [15][790/792]\tTime 1.835 (1425.432)\tETA 0:00:03\tLoss 0.5227 (0.5221)\n",
      "Epoch: [16][0/792]\tTime 1.777 (1.777)\tETA 0:23:27\tLoss 0.5221 (0.5221)\n",
      "Epoch: [16][5/792]\tTime 1.771 (10.705)\tETA 0:23:14\tLoss 0.5232 (0.5215)\n",
      "Epoch: [16][10/792]\tTime 1.852 (19.725)\tETA 0:24:08\tLoss 0.5210 (0.5213)\n",
      "Epoch: [16][15/792]\tTime 1.749 (28.560)\tETA 0:22:38\tLoss 0.5194 (0.5208)\n",
      "Epoch: [16][20/792]\tTime 1.741 (38.023)\tETA 0:22:23\tLoss 0.5206 (0.5208)\n",
      "Epoch: [16][25/792]\tTime 1.748 (46.853)\tETA 0:22:20\tLoss 0.5191 (0.5207)\n",
      "Epoch: [16][30/792]\tTime 1.795 (55.863)\tETA 0:22:47\tLoss 0.5219 (0.5208)\n",
      "Epoch: [16][35/792]\tTime 1.769 (64.808)\tETA 0:22:18\tLoss 0.5207 (0.5209)\n",
      "Epoch: [16][40/792]\tTime 1.821 (73.793)\tETA 0:22:49\tLoss 0.5193 (0.5209)\n",
      "Epoch: [16][45/792]\tTime 1.757 (82.725)\tETA 0:21:52\tLoss 0.5205 (0.5209)\n",
      "Epoch: [16][50/792]\tTime 1.726 (91.609)\tETA 0:21:20\tLoss 0.5199 (0.5209)\n",
      "Epoch: [16][55/792]\tTime 1.918 (100.756)\tETA 0:23:33\tLoss 0.5230 (0.5210)\n",
      "Epoch: [16][60/792]\tTime 1.782 (109.701)\tETA 0:21:44\tLoss 0.5193 (0.5211)\n",
      "Epoch: [16][65/792]\tTime 1.841 (118.851)\tETA 0:22:18\tLoss 0.5208 (0.5212)\n",
      "Epoch: [16][70/792]\tTime 1.734 (127.752)\tETA 0:20:51\tLoss 0.5205 (0.5212)\n",
      "Epoch: [16][75/792]\tTime 2.274 (137.300)\tETA 0:27:10\tLoss 0.5185 (0.5211)\n",
      "Epoch: [16][80/792]\tTime 1.862 (146.422)\tETA 0:22:06\tLoss 0.5204 (0.5211)\n",
      "Epoch: [16][85/792]\tTime 1.727 (155.536)\tETA 0:20:21\tLoss 0.5219 (0.5211)\n",
      "Epoch: [16][90/792]\tTime 1.784 (165.103)\tETA 0:20:52\tLoss 0.5211 (0.5212)\n",
      "Epoch: [16][95/792]\tTime 1.759 (174.110)\tETA 0:20:25\tLoss 0.5219 (0.5212)\n",
      "Epoch: [16][100/792]\tTime 1.859 (183.168)\tETA 0:21:26\tLoss 0.5193 (0.5212)\n",
      "Epoch: [16][105/792]\tTime 1.798 (192.209)\tETA 0:20:34\tLoss 0.5190 (0.5211)\n",
      "Epoch: [16][110/792]\tTime 1.774 (201.299)\tETA 0:20:09\tLoss 0.5211 (0.5211)\n",
      "Epoch: [16][115/792]\tTime 1.752 (210.171)\tETA 0:19:45\tLoss 0.5193 (0.5211)\n",
      "Epoch: [16][120/792]\tTime 1.754 (218.989)\tETA 0:19:38\tLoss 0.5185 (0.5210)\n",
      "Epoch: [16][125/792]\tTime 1.835 (228.039)\tETA 0:20:24\tLoss 0.5187 (0.5210)\n",
      "Epoch: [16][130/792]\tTime 1.788 (237.231)\tETA 0:19:43\tLoss 0.5191 (0.5209)\n",
      "Epoch: [16][135/792]\tTime 1.828 (246.132)\tETA 0:20:00\tLoss 0.5202 (0.5209)\n",
      "Epoch: [16][140/792]\tTime 1.756 (255.116)\tETA 0:19:04\tLoss 0.5214 (0.5209)\n",
      "Epoch: [16][145/792]\tTime 1.837 (264.334)\tETA 0:19:48\tLoss 0.5196 (0.5209)\n",
      "Epoch: [16][150/792]\tTime 1.776 (273.327)\tETA 0:19:00\tLoss 0.5188 (0.5209)\n",
      "Epoch: [16][155/792]\tTime 1.752 (282.223)\tETA 0:18:35\tLoss 0.5191 (0.5208)\n",
      "Epoch: [16][160/792]\tTime 1.737 (291.158)\tETA 0:18:17\tLoss 0.5215 (0.5208)\n",
      "Epoch: [16][165/792]\tTime 1.750 (300.841)\tETA 0:18:17\tLoss 0.5213 (0.5208)\n",
      "Epoch: [16][170/792]\tTime 1.884 (309.906)\tETA 0:19:31\tLoss 0.5198 (0.5208)\n",
      "Epoch: [16][175/792]\tTime 1.723 (318.966)\tETA 0:17:43\tLoss 0.5215 (0.5208)\n",
      "Epoch: [16][180/792]\tTime 1.853 (327.841)\tETA 0:18:53\tLoss 0.5207 (0.5208)\n",
      "Epoch: [16][185/792]\tTime 1.745 (336.760)\tETA 0:17:39\tLoss 0.5182 (0.5208)\n",
      "Epoch: [16][190/792]\tTime 1.761 (345.688)\tETA 0:17:40\tLoss 0.5218 (0.5208)\n",
      "Epoch: [16][195/792]\tTime 1.739 (354.531)\tETA 0:17:18\tLoss 0.5215 (0.5208)\n",
      "Epoch: [16][200/792]\tTime 1.724 (363.546)\tETA 0:17:00\tLoss 0.5237 (0.5208)\n",
      "Epoch: [16][205/792]\tTime 1.777 (372.448)\tETA 0:17:23\tLoss 0.5203 (0.5207)\n",
      "Epoch: [16][210/792]\tTime 1.789 (381.397)\tETA 0:17:21\tLoss 0.5204 (0.5208)\n",
      "Epoch: [16][215/792]\tTime 1.793 (390.349)\tETA 0:17:14\tLoss 0.5202 (0.5207)\n",
      "Epoch: [16][220/792]\tTime 1.853 (399.164)\tETA 0:17:39\tLoss 0.5209 (0.5207)\n",
      "Epoch: [16][225/792]\tTime 1.783 (407.985)\tETA 0:16:50\tLoss 0.5248 (0.5207)\n",
      "Epoch: [16][230/792]\tTime 1.818 (416.951)\tETA 0:17:01\tLoss 0.5237 (0.5208)\n",
      "Epoch: [16][235/792]\tTime 1.755 (425.695)\tETA 0:16:17\tLoss 0.5208 (0.5208)\n",
      "Epoch: [16][240/792]\tTime 1.774 (434.612)\tETA 0:16:19\tLoss 0.5243 (0.5209)\n",
      "Epoch: [16][245/792]\tTime 1.794 (443.707)\tETA 0:16:21\tLoss 0.5197 (0.5209)\n",
      "Epoch: [16][250/792]\tTime 1.755 (452.729)\tETA 0:15:51\tLoss 0.5228 (0.5209)\n",
      "Epoch: [16][255/792]\tTime 1.826 (461.685)\tETA 0:16:20\tLoss 0.5211 (0.5209)\n",
      "Epoch: [16][260/792]\tTime 1.814 (470.557)\tETA 0:16:04\tLoss 0.5221 (0.5209)\n",
      "Epoch: [16][265/792]\tTime 1.776 (479.513)\tETA 0:15:35\tLoss 0.5220 (0.5209)\n",
      "Epoch: [16][270/792]\tTime 1.806 (488.425)\tETA 0:15:42\tLoss 0.5227 (0.5210)\n",
      "Epoch: [16][275/792]\tTime 1.786 (497.295)\tETA 0:15:23\tLoss 0.5205 (0.5209)\n",
      "Epoch: [16][280/792]\tTime 1.797 (506.186)\tETA 0:15:19\tLoss 0.5233 (0.5209)\n",
      "Epoch: [16][285/792]\tTime 1.786 (514.986)\tETA 0:15:05\tLoss 0.5192 (0.5210)\n",
      "Epoch: [16][290/792]\tTime 1.759 (523.955)\tETA 0:14:43\tLoss 0.5197 (0.5209)\n",
      "Epoch: [16][295/792]\tTime 1.914 (533.176)\tETA 0:15:51\tLoss 0.5199 (0.5209)\n",
      "Epoch: [16][300/792]\tTime 1.818 (542.067)\tETA 0:14:54\tLoss 0.5187 (0.5209)\n",
      "Epoch: [16][305/792]\tTime 1.779 (551.014)\tETA 0:14:26\tLoss 0.5196 (0.5209)\n",
      "Epoch: [16][310/792]\tTime 1.864 (560.170)\tETA 0:14:58\tLoss 0.5212 (0.5209)\n",
      "Epoch: [16][315/792]\tTime 1.724 (569.137)\tETA 0:13:42\tLoss 0.5192 (0.5209)\n",
      "Epoch: [16][320/792]\tTime 1.799 (578.271)\tETA 0:14:08\tLoss 0.5246 (0.5209)\n",
      "Epoch: [16][325/792]\tTime 1.828 (587.321)\tETA 0:14:13\tLoss 0.5202 (0.5210)\n",
      "Epoch: [16][330/792]\tTime 2.035 (596.594)\tETA 0:15:40\tLoss 0.5234 (0.5210)\n",
      "Epoch: [16][335/792]\tTime 1.785 (605.438)\tETA 0:13:35\tLoss 0.5196 (0.5210)\n",
      "Epoch: [16][340/792]\tTime 1.777 (614.751)\tETA 0:13:23\tLoss 0.5209 (0.5210)\n",
      "Epoch: [16][345/792]\tTime 1.814 (623.818)\tETA 0:13:30\tLoss 0.5202 (0.5209)\n",
      "Epoch: [16][350/792]\tTime 1.822 (632.798)\tETA 0:13:25\tLoss 0.5201 (0.5209)\n",
      "Epoch: [16][355/792]\tTime 1.794 (641.935)\tETA 0:13:04\tLoss 0.5218 (0.5209)\n",
      "Epoch: [16][360/792]\tTime 1.856 (650.956)\tETA 0:13:21\tLoss 0.5207 (0.5209)\n",
      "Epoch: [16][365/792]\tTime 1.740 (659.959)\tETA 0:12:23\tLoss 0.5189 (0.5209)\n",
      "Epoch: [16][370/792]\tTime 1.759 (668.789)\tETA 0:12:22\tLoss 0.5195 (0.5209)\n",
      "Epoch: [16][375/792]\tTime 1.790 (677.636)\tETA 0:12:26\tLoss 0.5189 (0.5209)\n",
      "Epoch: [16][380/792]\tTime 1.821 (686.564)\tETA 0:12:30\tLoss 0.5235 (0.5209)\n",
      "Epoch: [16][385/792]\tTime 1.913 (695.633)\tETA 0:12:58\tLoss 0.5202 (0.5209)\n",
      "Epoch: [16][390/792]\tTime 1.851 (704.636)\tETA 0:12:24\tLoss 0.5211 (0.5209)\n",
      "Epoch: [16][395/792]\tTime 1.709 (713.639)\tETA 0:11:18\tLoss 0.5231 (0.5209)\n",
      "Epoch: [16][400/792]\tTime 1.787 (722.577)\tETA 0:11:40\tLoss 0.5247 (0.5210)\n",
      "Epoch: [16][405/792]\tTime 1.765 (731.344)\tETA 0:11:23\tLoss 0.5197 (0.5210)\n",
      "Epoch: [16][410/792]\tTime 1.803 (740.402)\tETA 0:11:28\tLoss 0.5198 (0.5209)\n",
      "Epoch: [16][415/792]\tTime 1.855 (749.465)\tETA 0:11:39\tLoss 0.5206 (0.5210)\n",
      "Epoch: [16][420/792]\tTime 1.778 (758.502)\tETA 0:11:01\tLoss 0.5204 (0.5209)\n",
      "Epoch: [16][425/792]\tTime 1.769 (767.458)\tETA 0:10:49\tLoss 0.5217 (0.5209)\n",
      "Epoch: [16][430/792]\tTime 1.756 (776.317)\tETA 0:10:35\tLoss 0.5203 (0.5209)\n",
      "Epoch: [16][435/792]\tTime 1.766 (785.217)\tETA 0:10:30\tLoss 0.5213 (0.5209)\n",
      "Epoch: [16][440/792]\tTime 1.746 (794.005)\tETA 0:10:14\tLoss 0.5226 (0.5209)\n",
      "Epoch: [16][445/792]\tTime 1.770 (802.964)\tETA 0:10:14\tLoss 0.5195 (0.5209)\n",
      "Epoch: [16][450/792]\tTime 1.847 (812.117)\tETA 0:10:31\tLoss 0.5223 (0.5209)\n",
      "Epoch: [16][455/792]\tTime 1.785 (821.005)\tETA 0:10:01\tLoss 0.5218 (0.5209)\n",
      "Epoch: [16][460/792]\tTime 1.771 (829.939)\tETA 0:09:47\tLoss 0.5232 (0.5209)\n",
      "Epoch: [16][465/792]\tTime 1.763 (838.982)\tETA 0:09:36\tLoss 0.5251 (0.5209)\n",
      "Epoch: [16][470/792]\tTime 1.738 (847.857)\tETA 0:09:19\tLoss 0.5223 (0.5210)\n",
      "Epoch: [16][475/792]\tTime 1.837 (856.775)\tETA 0:09:42\tLoss 0.5237 (0.5210)\n",
      "Epoch: [16][480/792]\tTime 1.841 (865.774)\tETA 0:09:34\tLoss 0.5197 (0.5210)\n",
      "Epoch: [16][485/792]\tTime 1.733 (874.517)\tETA 0:08:52\tLoss 0.5205 (0.5210)\n",
      "Epoch: [16][490/792]\tTime 1.748 (883.502)\tETA 0:08:47\tLoss 0.5234 (0.5210)\n",
      "Epoch: [16][495/792]\tTime 1.795 (892.285)\tETA 0:08:52\tLoss 0.5226 (0.5210)\n",
      "Epoch: [16][500/792]\tTime 1.767 (901.154)\tETA 0:08:35\tLoss 0.5210 (0.5210)\n",
      "Epoch: [16][505/792]\tTime 1.815 (910.163)\tETA 0:08:40\tLoss 0.5216 (0.5210)\n",
      "Epoch: [16][510/792]\tTime 1.748 (919.117)\tETA 0:08:12\tLoss 0.5209 (0.5210)\n",
      "Epoch: [16][515/792]\tTime 1.802 (928.201)\tETA 0:08:19\tLoss 0.5201 (0.5210)\n",
      "Epoch: [16][520/792]\tTime 1.874 (937.432)\tETA 0:08:29\tLoss 0.5238 (0.5211)\n",
      "Epoch: [16][525/792]\tTime 1.769 (946.402)\tETA 0:07:52\tLoss 0.5226 (0.5211)\n",
      "Epoch: [16][530/792]\tTime 1.777 (955.592)\tETA 0:07:45\tLoss 0.5240 (0.5211)\n",
      "Epoch: [16][535/792]\tTime 1.799 (964.299)\tETA 0:07:42\tLoss 0.5211 (0.5211)\n",
      "Epoch: [16][540/792]\tTime 1.854 (973.277)\tETA 0:07:47\tLoss 0.5290 (0.5211)\n",
      "Epoch: [16][545/792]\tTime 1.794 (982.187)\tETA 0:07:23\tLoss 0.5257 (0.5212)\n",
      "Epoch: [16][550/792]\tTime 1.745 (991.159)\tETA 0:07:02\tLoss 0.5262 (0.5212)\n",
      "Epoch: [16][555/792]\tTime 1.982 (1000.233)\tETA 0:07:49\tLoss 0.5299 (0.5212)\n",
      "Epoch: [16][560/792]\tTime 1.749 (1009.106)\tETA 0:06:45\tLoss 0.5266 (0.5212)\n",
      "Epoch: [16][565/792]\tTime 1.826 (1017.953)\tETA 0:06:54\tLoss 0.5219 (0.5213)\n",
      "Epoch: [16][570/792]\tTime 1.866 (1027.031)\tETA 0:06:54\tLoss 0.5224 (0.5213)\n",
      "Epoch: [16][575/792]\tTime 1.764 (1035.947)\tETA 0:06:22\tLoss 0.5204 (0.5213)\n",
      "Epoch: [16][580/792]\tTime 1.773 (1044.965)\tETA 0:06:15\tLoss 0.5218 (0.5213)\n",
      "Epoch: [16][585/792]\tTime 1.744 (1053.870)\tETA 0:06:00\tLoss 0.5189 (0.5213)\n",
      "Epoch: [16][590/792]\tTime 1.739 (1062.790)\tETA 0:05:51\tLoss 0.5204 (0.5213)\n",
      "Epoch: [16][595/792]\tTime 1.780 (1071.750)\tETA 0:05:50\tLoss 0.5257 (0.5213)\n",
      "Epoch: [16][600/792]\tTime 1.845 (1080.974)\tETA 0:05:54\tLoss 0.5207 (0.5213)\n",
      "Epoch: [16][605/792]\tTime 1.786 (1089.865)\tETA 0:05:33\tLoss 0.5230 (0.5213)\n",
      "Epoch: [16][610/792]\tTime 1.774 (1098.804)\tETA 0:05:22\tLoss 0.5216 (0.5213)\n",
      "Epoch: [16][615/792]\tTime 1.801 (1107.795)\tETA 0:05:18\tLoss 0.5211 (0.5213)\n",
      "Epoch: [16][620/792]\tTime 1.795 (1116.927)\tETA 0:05:08\tLoss 0.5247 (0.5213)\n",
      "Epoch: [16][625/792]\tTime 1.855 (1126.315)\tETA 0:05:09\tLoss 0.5207 (0.5213)\n",
      "Epoch: [16][630/792]\tTime 1.729 (1135.610)\tETA 0:04:40\tLoss 0.5223 (0.5213)\n",
      "Epoch: [16][635/792]\tTime 1.770 (1144.588)\tETA 0:04:37\tLoss 0.5245 (0.5213)\n",
      "Epoch: [16][640/792]\tTime 1.717 (1153.374)\tETA 0:04:21\tLoss 0.5247 (0.5214)\n",
      "Epoch: [16][645/792]\tTime 1.776 (1162.207)\tETA 0:04:21\tLoss 0.5210 (0.5214)\n",
      "Epoch: [16][650/792]\tTime 1.729 (1170.859)\tETA 0:04:05\tLoss 0.5219 (0.5214)\n",
      "Epoch: [16][655/792]\tTime 1.745 (1179.751)\tETA 0:03:59\tLoss 0.5210 (0.5214)\n",
      "Epoch: [16][660/792]\tTime 1.845 (1188.798)\tETA 0:04:03\tLoss 0.5195 (0.5214)\n",
      "Epoch: [16][665/792]\tTime 1.775 (1197.617)\tETA 0:03:45\tLoss 0.5199 (0.5214)\n",
      "Epoch: [16][670/792]\tTime 1.787 (1206.617)\tETA 0:03:38\tLoss 0.5212 (0.5214)\n",
      "Epoch: [16][675/792]\tTime 1.840 (1215.663)\tETA 0:03:35\tLoss 0.5213 (0.5214)\n",
      "Epoch: [16][680/792]\tTime 1.802 (1224.598)\tETA 0:03:21\tLoss 0.5224 (0.5214)\n",
      "Epoch: [16][685/792]\tTime 2.039 (1233.979)\tETA 0:03:38\tLoss 0.5217 (0.5214)\n",
      "Epoch: [16][690/792]\tTime 1.798 (1242.856)\tETA 0:03:03\tLoss 0.5216 (0.5214)\n",
      "Epoch: [16][695/792]\tTime 1.889 (1251.915)\tETA 0:03:03\tLoss 0.5206 (0.5214)\n",
      "Epoch: [16][700/792]\tTime 1.696 (1260.848)\tETA 0:02:36\tLoss 0.5179 (0.5213)\n",
      "Epoch: [16][705/792]\tTime 1.806 (1269.677)\tETA 0:02:37\tLoss 0.5199 (0.5213)\n",
      "Epoch: [16][710/792]\tTime 1.776 (1278.557)\tETA 0:02:25\tLoss 0.5180 (0.5213)\n",
      "Epoch: [16][715/792]\tTime 1.731 (1287.366)\tETA 0:02:13\tLoss 0.5185 (0.5213)\n",
      "Epoch: [16][720/792]\tTime 1.719 (1296.206)\tETA 0:02:03\tLoss 0.5174 (0.5213)\n",
      "Epoch: [16][725/792]\tTime 1.741 (1305.048)\tETA 0:01:56\tLoss 0.5205 (0.5213)\n",
      "Epoch: [16][730/792]\tTime 1.787 (1313.922)\tETA 0:01:50\tLoss 0.5244 (0.5213)\n",
      "Epoch: [16][735/792]\tTime 1.775 (1322.857)\tETA 0:01:41\tLoss 0.5191 (0.5213)\n",
      "Epoch: [16][740/792]\tTime 1.793 (1331.705)\tETA 0:01:33\tLoss 0.5188 (0.5213)\n",
      "Epoch: [16][745/792]\tTime 1.758 (1340.838)\tETA 0:01:22\tLoss 0.5217 (0.5213)\n",
      "Epoch: [16][750/792]\tTime 1.838 (1349.813)\tETA 0:01:17\tLoss 0.5175 (0.5213)\n",
      "Epoch: [16][755/792]\tTime 1.757 (1358.737)\tETA 0:01:05\tLoss 0.5208 (0.5213)\n",
      "Epoch: [16][760/792]\tTime 1.770 (1367.575)\tETA 0:00:56\tLoss 0.5257 (0.5213)\n",
      "Epoch: [16][765/792]\tTime 1.849 (1376.526)\tETA 0:00:49\tLoss 0.5234 (0.5213)\n",
      "Epoch: [16][770/792]\tTime 1.831 (1385.545)\tETA 0:00:40\tLoss 0.5194 (0.5213)\n",
      "Epoch: [16][775/792]\tTime 1.756 (1394.309)\tETA 0:00:29\tLoss 0.5183 (0.5213)\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "import argparse\n",
    "import datetime\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.utils as utils\n",
    "import torchvision.utils as vutils    \n",
    "from tensorboardX import SummaryWriter\n",
    "\n",
    "# from data import getTrainingTestingData\n",
    "# from utils import AverageMeter, DepthNorm, colorize\n",
    "\n",
    "model = Model().cuda()\n",
    "if torch.cuda.device_count() > 1:\n",
    "  print(\"Let's use\", torch.cuda.device_count(), \"GPUs!\")\n",
    "  # dim = 0 [30, xxx] -> [10, ...], [10, ...], [10, ...] on 3 GPUs\n",
    "  model = nn.DataParallel(model)\n",
    "#load trained model if needed\n",
    "#model.load_state_dict(torch.load('/workspace/1.pth'))\n",
    "print('Model created.')\n",
    "\n",
    "epochs=50\n",
    "lr=0.0001\n",
    "batch_size=64\n",
    "\n",
    "depth_dataset = DepthDataset(traincsv=traincsv, root_dir='/workspace/',\n",
    "                transform=transforms.Compose([Augmentation(0.5),ToTensor()]))\n",
    "train_loader=DataLoader(depth_dataset, batch_size, shuffle=True)\n",
    "l1_criterion = nn.L1Loss()\n",
    "\n",
    "optimizer = torch.optim.Adam( model.parameters(), lr )\n",
    "\n",
    "# Start training...\n",
    "for epoch in range(epochs):\n",
    "    path='/workspace/'+str(epoch)+'.pth'        \n",
    "    torch.save(model.state_dict(), path)\n",
    "    batch_time = AverageMeter()\n",
    "    losses = AverageMeter()\n",
    "    N = len(train_loader)\n",
    "\n",
    "    # Switch to train mode\n",
    "    model.train()\n",
    "\n",
    "    end = time.time()\n",
    "\n",
    "    for i, sample_batched in enumerate(train_loader):\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        #Prepare sample and target\n",
    "        image = torch.autograd.Variable(sample_batched['image'].cuda())\n",
    "        depth = torch.autograd.Variable(sample_batched['depth'].cuda(non_blocking=True))\n",
    "\n",
    "        # Normalize depth\n",
    "        depth_n = DepthNorm( depth )\n",
    "\n",
    "        # Predict\n",
    "        output = model(image)\n",
    "\n",
    "        # Compute the loss\n",
    "        l_depth = l1_criterion(output, depth_n)\n",
    "        l_ssim = torch.clamp((1 - ssim(output, depth_n, val_range = 1000.0 / 10.0)) * 0.5, 0, 1)\n",
    "\n",
    "        loss = (1.0 * l_ssim.mean().item()) + (0.1 * l_depth)\n",
    "\n",
    "        # Update step\n",
    "       \n",
    "        losses.update(loss.data.item(), image.size(0))\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        # Measure elapsed time\n",
    "        batch_time.update(time.time() - end)\n",
    "        end = time.time()\n",
    "        eta = str(datetime.timedelta(seconds=int(batch_time.val*(N - i))))\n",
    "\n",
    "        # Log progress\n",
    "        niter = epoch*N+i\n",
    "        if i % 5 == 0:\n",
    "            # Print to console\n",
    "            print('Epoch: [{0}][{1}/{2}]\\t'\n",
    "            'Time {batch_time.val:.3f} ({batch_time.sum:.3f})\\t'\n",
    "            'ETA {eta}\\t'\n",
    "            'Loss {loss.val:.4f} ({loss.avg:.4f})'\n",
    "            .format(epoch, i, N, batch_time=batch_time, loss=losses, eta=eta))\n",
    "\n",
    "            # Log to tensorboard\n",
    "            #writer.add_scalar('Train/Loss', losses.val, niter)\n",
    "            \n",
    "    path='/workspace/'+str(epoch)+'.pth'        \n",
    "    torch.save(model.state_dict(), path)    \n",
    "        if i % 300 == 0:\n",
    "            LogProgress(model, writer, test_loader, niter)\n",
    "\n",
    "    # Record epoch's intermediate results\n",
    "    LogProgress(model, writer, test_loader, niter)\n",
    "    writer.add_scalar('Train/Loss.avg', losses.avg, epoch)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Evaluations\n",
    "\n",
    "model = Model().cuda()\n",
    "model = nn.DataParallel(model)\n",
    "#load the model if needed\n",
    "#model.load_state_dict(torch.load('/workspace/3.pth'))\n",
    "model.eval()\n",
    "batch_size=1\n",
    "\n",
    "depth_dataset = DepthDataset(traincsv=traincsv, root_dir='/workspace/',\n",
    "                transform=transforms.Compose([Augmentation(0.5),ToTensor()]))\n",
    "train_loader=DataLoader(depth_dataset, batch_size, shuffle=True)\n",
    "\n",
    "for sample_batched1  in (train_loader):\n",
    "    image1 = torch.autograd.Variable(sample_batched1['image'].cuda())\n",
    "    \n",
    "    outtt=model(image1 )\n",
    "    break\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ploting the evaluated images\n",
    "\n",
    "x=outtt.detach().cpu().numpy()\n",
    "x.shape\n",
    "x=x.reshape(240,320)\n",
    "plt.imshow(x)\n",
    "plt.figure()\n",
    "plt.imshow(sample_batched1['image'].detach().cpu().numpy().reshape(3,480,640).transpose(1,2,0))"
   ]
  }
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